Self-Driving Laboratories for Chemistry and Materials Science

Tom, Gary; Schmid, Stefan P.; Baird, Sterling G.; Cao, Yang; Darvish, Kourosh; Hao, Han; Lo, Stanley; Pablo-García, Sergio; Rajaonson, Ella M.; Skreta, Marta; Yoshikawa, Naruki; Corapi, Samantha; Akkoc, Gun Deniz; Strieth-Kalthoff, Felix; Seifrid, Martin; Aspuru-Guzik, Alán

doi:10.1021/acs.chemrev.4c00055

Title: Self-Driving Laboratories for Chemistry and Materials Science

Journal Article · Tue Aug 13 00:00:00 UTC 2024 · Chemical Reviews

DOI: https://doi.org/10.1021/acs.chemrev.4c00055 · OSTI ID:2438394

^[1];

^[2]; Baird, Sterling G. ^[3];

^[4]; Darvish, Kourosh ^[5]; Hao, Han ^[4];

^[6]; Pablo-García, Sergio ^[6]; Rajaonson, Ella M. ^[1]; Skreta, Marta ^[1];

^[1]; Corapi, Samantha ^[6]; Akkoc, Gun Deniz ^[7]; Strieth-Kalthoff, Felix ^[8];

^[9];

^[10]

University of Toronto, ON (Canada); Vector Institute for Artificial Intelligence, Toronto, ON (Canada)
ETH Zurich (Switzerland)
Acceleration Consortium, Toronto, ON (Canada)
University of Toronto, ON (Canada); Acceleration Consortium, Toronto, ON (Canada)
University of Toronto, ON (Canada); Vector Institute for Artificial Intelligence, Toronto, ON (Canada); Acceleration Consortium, Toronto, ON (Canada)
University of Toronto, ON (Canada)
Forschungszentrum Jülich GmbH, Erlangen (Germany); Friedrich-Alexander Universität Erlangen-Nürnberg (Germany)
University of Toronto, ON (Canada); University of Wuppertal (Germany)
University of Toronto, ON (Canada); North Carolina State University, Raleigh, NC (United States)
University of Toronto, ON (Canada); Vector Institute for Artificial Intelligence, Toronto, ON (Canada); Acceleration Consortium, Toronto, ON (Canada); Lebovic Fellow, Canadian Institute for Advanced Research (CIFAR), Toronto, ON (Canada)

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.

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Research Organization:: University of Minnesota, Minneapolis, MN (United States)

Sponsoring Organization:: Defense Advanced Research Projects Agency (DARPA); USDOE; USDOE Office of Science (SC)

Grant/Contract Number:: SC0023454

OSTI ID:: 2438394

Journal Information:: Chemical Reviews, Journal Name: Chemical Reviews Journal Issue: 16 Vol. 124; ISSN 1520-6890; ISSN 0009-2665

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

References (569)

Combinatorial Approach to the Hydrothermal Synthesis of Zeolites Akporiaye, Duncan E.; Dahl, Ivar M.; Karlsson, Arne Angewandte Chemie International Edition, Vol. 37, Issue 5, p. 609-611 https://doi.org/10.1002/(SICI)1521-3773(19980316)37:5<609::AID-ANIE609>3.0.CO;2-X	journal	March 1998
Facile Room‐Temperature Anion Exchange Reactions of Inorganic Perovskite Quantum Dots Enabled by a Modular Microfluidic Platform Abdel‐Latif, Kameel; Epps, Robert W.; Kerr, Corwin B. Advanced Functional Materials, Vol. 29, Issue 23 https://doi.org/10.1002/adfm.201900712	journal	March 2019
Self‐Driving Platform for Metal Nanoparticle Synthesis: Combining Microfluidics and Machine Learning Tao, Huachen; Wu, Tianyi; Kheiri, Sina Advanced Functional Materials, Vol. 31, Issue 51 https://doi.org/10.1002/adfm.202106725	journal	September 2021
Beyond Ternary OPV: High‐Throughput Experimentation and Self‐Driving Laboratories Optimize Multicomponent Systems Langner, Stefan; Häse, Florian; Perea, José Darío Advanced Materials, Vol. 32, Issue 14 https://doi.org/10.1002/adma.201907801	journal	April 2020
Artificial Chemist: An Autonomous Quantum Dot Synthesis Bot Epps, Robert W.; Bowen, Michael S.; Volk, Amanda A. Advanced Materials, Vol. 32, Issue 30 https://doi.org/10.1002/adma.202001626	journal	June 2020
Integrating Computational and Experimental Workflows for Accelerated Organic Materials Discovery Greenaway, Rebecca L.; Jelfs, Kim E. Advanced Materials, Vol. 33, Issue 11 https://doi.org/10.1002/adma.202004831	journal	February 2021
Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials Libraries Ziatdinov, Maxim A.; Liu, Yongtao; Morozovska, Anna N. Advanced Materials, Vol. 34, Issue 20 https://doi.org/10.1002/adma.202201345	journal	April 2022
Machine Learning on a Robotic Platform for the Design of Polymer–Protein Hybrids Tamasi, Matthew J.; Patel, Roshan A.; Borca, Carlos H. Advanced Materials, Vol. 34, Issue 30 https://doi.org/10.1002/adma.202201809	journal	June 2022
Enabling Modular Autonomous Feedback‐Loops in Materials Science through Hierarchical Experimental Laboratory Automation and Orchestration Rahmanian, Fuzhan; Flowers, Jackson; Guevarra, Dan Advanced Materials Interfaces, Vol. 9, Issue 8 https://doi.org/10.1002/admi.202101987	journal	January 2022
Direct C−H Arylation of Indole‐3‐Acetic Acid Derivatives Enabled by an Autonomous Self‐Optimizing Flow Reactor Vasudevan, N.; Wimmer, Eric; Barré, Elvina Advanced Synthesis & Catalysis, Vol. 363, Issue 3 https://doi.org/10.1002/adsc.202001217	journal	November 2020
High‐Throughput Experimentation and Computational Freeway Lanes for Accelerated Battery Electrolyte and Interface Development Research Benayad, Anass; Diddens, Diddo; Heuer, Andreas Advanced Energy Materials, Vol. 12, Issue 17 https://doi.org/10.1002/aenm.202102678	journal	November 2021
Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries Bhowmik, Arghya; Berecibar, Maitane; Casas‐Cabanas, Montse Advanced Energy Materials, Vol. 12, Issue 17 https://doi.org/10.1002/aenm.202102698	journal	November 2021
Self‐Driven Multistep Quantum Dot Synthesis Enabled by Autonomous Robotic Experimentation in Flow Abdel-Latif, Kameel; Epps, Robert W.; Bateni, Fazel Advanced Intelligent Systems, Vol. 3, Issue 2 https://doi.org/10.1002/aisy.202000245	journal	December 2020
Autonomous Nanocrystal Doping by Self‐Driving Fluidic Micro‐Processors Bateni, Fazel; Epps, Robert W.; Antami, Kameel Advanced Intelligent Systems, Vol. 4, Issue 5 https://doi.org/10.1002/aisy.202200017	journal	March 2022
Toward a Computational Tool Predicting the Stereochemical Outcome of Asymmetric Reactions: Development and Application of a Rapid and Accurate Program Based on Organic Principles Corbeil, Christopher R.; Thielges, Sabine; Schwartzentruber, Jeremy A. Angewandte Chemie International Edition, Vol. 47, Issue 14 https://doi.org/10.1002/anie.200704774	journal	March 2008
An Integrated Microreactor System for Self-Optimization of a Heck Reaction: From Micro- to Mesoscale Flow Systems McMullen, Jonathan P.; Stone, Matthew T.; Buchwald, Stephen L. Angewandte Chemie International Edition, Vol. 49, Issue 39 https://doi.org/10.1002/anie.201002590	journal	August 2010
Self-Optimizing Continuous Reactions in Supercritical Carbon Dioxide Parrott, Andrew J.; Bourne, Richard A.; Akien, Geoffrey R. Angewandte Chemie International Edition, Vol. 50, Issue 16 https://doi.org/10.1002/anie.201100412	journal	March 2011
Continuous-Flow Technology-A Tool for the Safe Manufacturing of Active Pharmaceutical Ingredients Gutmann, Bernhard; Cantillo, David; Kappe, C. Oliver Angewandte Chemie International Edition, Vol. 54, Issue 23 https://doi.org/10.1002/anie.201409318	journal	May 2015
Reproducibility in Chemical Research Bergman, Robert G.; Danheiser, Rick L. Angewandte Chemie International Edition, Vol. 55, Issue 41 https://doi.org/10.1002/anie.201606591	journal	August 2016
Human versus Robots in the Discovery and Crystallization of Gigantic Polyoxometalates Duros, Vasilios; Grizou, Jonathan; Xuan, Weimin Angewandte Chemie International Edition, Vol. 56, Issue 36 https://doi.org/10.1002/anie.201705721	journal	August 2017
Across‐the‐World Automated Optimization and Continuous‐Flow Synthesis of Pharmaceutical Agents Operating Through a Cloud‐Based Server Fitzpatrick, Daniel E.; Maujean, Timothé; Evans, Amanda C. Angewandte Chemie International Edition, Vol. 57, Issue 46 https://doi.org/10.1002/anie.201809080	journal	November 2018
Precise Polymer Synthesis by Autonomous Self‐Optimizing Flow Reactors Rubens, Maarten; Vrijsen, Jeroen H.; Laun, Joachim Angewandte Chemie International Edition, Vol. 58, Issue 10 https://doi.org/10.1002/anie.201810384	journal	March 2019
From Concept to Crystals via Prediction: Multi‐Component Organic Cage Pots by Social Self‐Sorting Greenaway, Rebecca L.; Santolini, Valentina; Pulido, Angeles Angewandte Chemie International Edition, Vol. 58, Issue 45 https://doi.org/10.1002/anie.201909237	journal	September 2019
MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning** Luo, Yi; Bag, Saientan; Zaremba, Orysia Angewandte Chemie International Edition, Vol. 61, Issue 19 https://doi.org/10.1002/anie.202200242	journal	March 2022
One‐Shot Active Learning for Globally Optimal Battery Electrolyte Conductivity** Rahmanian, Fuzhan; Vogler, Monika; Wölke, Christian Batteries & Supercaps, Vol. 5, Issue 10 https://doi.org/10.1002/batt.202200228	journal	August 2022
Multi‐Step Continuous‐Flow Organic Synthesis: Opportunities and Challenges Jiao, Jiao; Nie, Wenzheng; Yu, Tao Chemistry – A European Journal, Vol. 27, Issue 15 https://doi.org/10.1002/chem.202004477	journal	January 2021
Advancements in droplet reactor systems represent new opportunities in chemical reactor engineering: A perspective Zhang, Meng; Vokoun, Asher E.; Chen, Boyuan The Canadian Journal of Chemical Engineering, Vol. 101, Issue 9 https://doi.org/10.1002/cjce.24897	journal	March 2023
A Machine Learning‐Enabled Autonomous Flow Chemistry Platform for Process Optimization of Multiple Reaction Metrics Jeraal, Mohammed I.; Sung, Simon; Lapkin, Alexei A. Chemistry–Methods, Vol. 1, Issue 1 https://doi.org/10.1002/cmtd.202000044	journal	December 2020
FireWorks: a dynamic workflow system designed for high-throughput applications: FireWorks: A Dynamic Workflow System Designed for High-Throughput Applications Jain, Anubhav; Ong, Shyue Ping; Chen, Wei Concurrency and Computation: Practice and Experience, Vol. 27, Issue 17 https://doi.org/10.1002/cpe.3505	journal	May 2015
Rapid Synthesis of Zeolitic Imidazole Frameworks in Laser‐Induced Graphene Microreactors Xie, Yunchao; Zhang, Chi; Su, Jheng‐Wun ChemSusChem, Vol. 12, Issue 2 https://doi.org/10.1002/cssc.201802446	journal	January 2019
Self-Optimizing Reactor Systems: Algorithms, On-line Analytics, Setups, and Strategies for Accelerating Continuous Flow Process Optimization Fabry, David C.; Sugiono, Erli; Rueping, Magnus Israel Journal of Chemistry, Vol. 54, Issue 4 https://doi.org/10.1002/ijch.201300080	journal	November 2013
PyADF - A scripting framework for multiscale quantum chemistry Jacob, Christoph R.; Beyhan, S. Maya; Bulo, Rosa E. Journal of Computational Chemistry, Vol. 32, Issue 10 https://doi.org/10.1002/jcc.21810	journal	May 2011
molSimplify: A toolkit for automating discovery in inorganic chemistry Ioannidis, Efthymios I.; Gani, Terry Z. H.; Kulik, Heather J. Journal of Computational Chemistry, Vol. 37, Issue 22 https://doi.org/10.1002/jcc.24437	journal	July 2016
Chemoinformatics as a Theoretical Chemistry Discipline Varnek, Alexandre; Baskin, Igor I. Molecular Informatics, Vol. 30, Issue 1 https://doi.org/10.1002/minf.201000100	journal	January 2011
Facilitating quality control for spectra assignments of small organic molecules: nmrshiftdb2 - a free in-house NMR database with integrated LIMS for academic service laboratories: Lab administration, spectra assignment aid and local database Kuhn, Stefan; Schlörer, Nils E. Magnetic Resonance in Chemistry, Vol. 53, Issue 8 https://doi.org/10.1002/mrc.4263	journal	May 2015
High‐Throughput Robotic Synthesis and Photoluminescence Characterization of Aqueous Multinary Copper–Silver Indium Chalcogenide Quantum Dots Stroyuk, Oleksandr; Raievska, Oleksandra; Langner, Stefan Particle & Particle Systems Characterization, Vol. 38, Issue 10 https://doi.org/10.1002/ppsc.202100169	journal	September 2021
C hem S hell—a modular software package for QM / MM simulations Metz, Sebastian; Kästner, Johannes; Sokol, Alexey A. Wiley Interdisciplinary Reviews: Computational Molecular Science, Vol. 4, Issue 2 https://doi.org/10.1002/wcms.1163	journal	July 2013
AQME: Automated quantum mechanical environments for researchers and educators Alegre‐Requena, Juan V.; Sowndarya S. V., Shree; Pérez‐Soto, Raúl WIREs Computational Molecular Science https://doi.org/10.1002/wcms.1663	journal	February 2023
On bayesian methods for seeking the extremum Močkus, J. Optimization Techniques IFIP Technical Conference Novosibirsk, July 1–7, 1974 https://doi.org/10.1007/3-540-07165-2_55	book	January 1975
The CMA Evolution Strategy: A Comparing Review Hansen, Nikolaus Towards a New Evolutionary Computation https://doi.org/10.1007/3-540-32494-1_4	book	January 2007
Sequential Model-Based Optimization for General Algorithm Configuration Hutter, Frank; Hoos, Holger H.; Leyton-Brown, Kevin Lecture Notes in Computer Science https://doi.org/10.1007/978-3-642-25566-3_40	book	January 2011
The origins of kriging Cressie, Noel Mathematical Geology, Vol. 22, Issue 3 https://doi.org/10.1007/BF00889887	journal	April 1990
Support-vector networks Cortes, Corinna; Vapnik, Vladimir Machine Learning, Vol. 20, Issue 3 https://doi.org/10.1007/BF00994018	journal	September 1995
Laboratory robotics ? Past, present, and future Owens, Grover D.; Eckstein, Rodney J.; Franz, Thomas P. Mikrochimica Acta, Vol. 89, Issue 1-6 https://doi.org/10.1007/BF01207305	journal	January 1986
Unattended optimisation of normal phase high-performance liquid chromatography separations with a microcomputer controlled chromatograph Berridge, J. C. Chromatographia, Vol. 16, Issue 1 https://doi.org/10.1007/BF02258892	journal	December 1982
Sequential simplex optimization and multichannel detection in HPLC: Application to method development Wright, A. G.; Fell, A. F.; Berridge, J. C. Chromatographia, Vol. 24, Issue 1 https://doi.org/10.1007/BF02688540	journal	December 1987
The synthesis of ammonia from its elements Nobel Lecture, June 2, 1920 Haber, Fritz Resonance, Vol. 7, Issue 9 https://doi.org/10.1007/BF02836189	journal	September 2002
Engineering microbial factories for synthesis of value-added products Du, Jing; Shao, Zengyi; Zhao, Huimin Journal of Industrial Microbiology & Biotechnology, Vol. 38, Issue 8 https://doi.org/10.1007/s10295-011-0970-3	journal	April 2011
Machine Learning in Drug Discovery: A Review Dara, Suresh; Dhamercherla, Swetha; Jadav, Surender Singh Artificial Intelligence Review, Vol. 55, Issue 3 https://doi.org/10.1007/s10462-021-10058-4	journal	August 2021
Large language models for chemistry robotics Yoshikawa, Naruki; Skreta, Marta; Darvish, Kourosh Autonomous Robots, Vol. 47, Issue 8 https://doi.org/10.1007/s10514-023-10136-2	journal	October 2023
Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm Bradford, Eric; Schweidtmann, Artur M.; Lapkin, Alexei Journal of Global Optimization, Vol. 71, Issue 2 https://doi.org/10.1007/s10898-018-0609-2	journal	February 2018
MVMOO: Mixed variable multi-objective optimisation Manson, Jamie A.; Chamberlain, Thomas W.; Bourne, Richard A. Journal of Global Optimization, Vol. 80, Issue 4 https://doi.org/10.1007/s10898-021-01052-9	journal	July 2021
A Comprehensive Review on Multi-objective Optimization Techniques: Past, Present and Future Sharma, Shubhkirti; Kumar, Vijay Archives of Computational Methods in Engineering, Vol. 29, Issue 7 https://doi.org/10.1007/s11831-022-09778-9	journal	July 2022
Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD) Saal, James E.; Kirklin, Scott; Aykol, Muratahan JOM, Vol. 65, Issue 11 https://doi.org/10.1007/s11837-013-0755-4	journal	September 2013
Towards Automated Design of Corrosion Resistant Alloy Coatings with an Autonomous Scanning Droplet Cell DeCost, Brian; Joress, Howie; Sarker, Suchismita JOM, Vol. 74, Issue 8 https://doi.org/10.1007/s11837-022-05367-0	journal	June 2022
Solid-State Electrolytes for Lithium-Ion Batteries: Fundamentals, Challenges and Perspectives Zhao, Wenjia; Yi, Jin; He, Ping Electrochemical Energy Reviews, Vol. 2, Issue 4 https://doi.org/10.1007/s41918-019-00048-0	journal	August 2019
Applications of laboratory robotics in spectrophotometric sample preparation and experimental optimization Lochmüller, C. H.; Lung, K. R. Analytica Chimica Acta, Vol. 183 https://doi.org/10.1016/0003-2670(86)80094-0	journal	January 1986
Classifier systems and genetic algorithms Booker, L. B.; Goldberg, D. E.; Holland, J. H. Artificial Intelligence, Vol. 40, Issue 1-3 https://doi.org/10.1016/0004-3702(89)90050-7	journal	September 1989
Chemical process optimization using Newton-like methods Lucia, A.; Xu, J. Computers & Chemical Engineering, Vol. 14, Issue 2 https://doi.org/10.1016/0098-1354(90)87072-W	journal	February 1990
A genetic algorithmic framework for process design and optimization Androulakis, I. P.; Venkatasubramanian, V. Computers & Chemical Engineering, Vol. 15, Issue 4 https://doi.org/10.1016/0098-1354(91)85009-J	journal	April 1991
Techniques for the automated optimization of HPLC separations Berridge, John C. TrAC Trends in Analytical Chemistry, Vol. 3, Issue 1 https://doi.org/10.1016/0165-9936(84)80026-6	journal	January 1984
Analysis of variance (ANOVA) St»hle, Lars; Wold, Svante Chemometrics and Intelligent Laboratory Systems, Vol. 6, Issue 4 https://doi.org/10.1016/0169-7439(89)80095-4	journal	November 1989
A retrospective on the automation of laboratory synthetic chemistry Lindsey, Jonathan S. Chemometrics and Intelligent Laboratory Systems, Vol. 17, Issue 1 https://doi.org/10.1016/0169-7439(92)90025-B	journal	October 1992
Experiment planner for strategic experimentation with an automated chemistry workstation Plouvier, Jean-Christophe; Andrew Corkan, L.; Lindsey, Jonathan S. Chemometrics and Intelligent Laboratory Systems, Vol. 17, Issue 1 https://doi.org/10.1016/0169-7439(92)90027-D	journal	October 1992
Application of an automated chemistry workstation to problems in synthetic chemistry Andrew Corkan, L.; Plouvier, Jean-Christophe; Lindsey, Jonathan S. Chemometrics and Intelligent Laboratory Systems, Vol. 17, Issue 1 https://doi.org/10.1016/0169-7439(92)90028-E	journal	October 1992
Advances in laboratory robotics for automated sample preparation Little, James N. Chemometrics and Intelligent Laboratory Systems, Vol. 21, Issue 2-3 https://doi.org/10.1016/0169-7439(93)89010-8	journal	December 1993
Absorption and emission spectroscopic investigation of cyanovinyldiethylaniline dye vapors Deshpande, A. V.; Beidoun, A.; Penzkofer, A. Chemical Physics, Vol. 142, Issue 1 https://doi.org/10.1016/0301-0104(90)89075-2	journal	March 1990
Chemical process optimization by computer — a self-directed chemical synthesis system Winicov, H.; Schainbaum, J.; Buckley, J. Analytica Chimica Acta, Vol. 103, Issue 4 https://doi.org/10.1016/S0003-2670(01)83110-X	journal	December 1978
Continuous optimization using a dynamic simplex method Xiong, Qiang; Jutan, Arthur Chemical Engineering Science, Vol. 58, Issue 16 https://doi.org/10.1016/S0009-2509(03)00236-7	journal	August 2003
Unattended optimisation of reversed-phase high-performance liquid chromatographic separations using the modified simplex algorithm Berridge, J. C. Journal of Chromatography A, Vol. 244, Issue 1 https://doi.org/10.1016/S0021-9673(00)80117-X	journal	July 1982
Minimizing the time for gas chromatographic analysis Holderith, József; Tóth, Tibor; Váradi, András Journal of Chromatography A, Vol. 119 https://doi.org/10.1016/S0021-9673(00)86784-9	journal	April 1976
Intelligent automation of high-performance liquid chromatography method development by means of a real-time knowledge-based approach I., Ting-Po; Smith, Randy; Guhan, Sam Journal of Chromatography A, Vol. 972, Issue 1 https://doi.org/10.1016/S0021-9673(02)01075-0	journal	September 2002
Methanol oxidation and direct methanol fuel cells: a selective review Wasmus, S.; Küver, A. Journal of Electroanalytical Chemistry, Vol. 461, Issue 1-2, p. 14-31 https://doi.org/10.1016/S0022-0728(98)00197-1	journal	January 1999
Optimization of a flow injection analysis system for tartaric acid determination in wines Silva, H. Talanta, Vol. 58, Issue 6 https://doi.org/10.1016/S0039-9140(02)00436-8	journal	December 2002
Dynamic optimization of semicontinuous emulsion copolymerization reactions: composition and molecular weight distribution Sayer, C.; Arzamendi, G.; Asua, J. M. Computers & Chemical Engineering, Vol. 25, Issue 4-6 https://doi.org/10.1016/S0098-1354(01)00658-5	journal	May 2001
Flow-injection spectrophotometric determination of periodate and iodate by their reaction with pyrogallol red in acidic media Ensafi, Ali A.; Chamjangali, M. Arab Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, Vol. 58, Issue 13 https://doi.org/10.1016/S1386-1425(02)00032-X	journal	November 2002
Simplex optimization and kinetic determination of nabumetone in pharmaceutical preparations by micellar—stabilized room temperature phosphorescence Pulgarín, J. A. Murillo; Molina, A. Alañón; Pardo, M. T. Alañón Analytica Chimica Acta, Vol. 528, Issue 1 https://doi.org/10.1016/j.aca.2004.10.014	journal	January 2005
Closed-loop automatic gradient design for liquid chromatography using Bayesian optimization Boelrijk, Jim; Ensing, Bernd; Forré, Patrick Analytica Chimica Acta, Vol. 1242 https://doi.org/10.1016/j.aca.2023.340789	journal	February 2023
High-throughput rapid experimental alloy development (HT-READ) Vecchio, Kenneth S.; Dippo, Olivia F.; Kaufmann, Kevin R. Acta Materialia, Vol. 221 https://doi.org/10.1016/j.actamat.2021.117352	journal	December 2021
Structural and functional stabilization of protein entities: state-of-the-art Balcão, Victor M.; Vila, Marta M. D. C. Advanced Drug Delivery Reviews, Vol. 93 https://doi.org/10.1016/j.addr.2014.10.005	journal	October 2015
Revolutionizing drug formulation development: The increasing impact of machine learning Bao, Zeqing; Bufton, Jack; Hickman, Riley J. Advanced Drug Delivery Reviews, Vol. 202 https://doi.org/10.1016/j.addr.2023.115108	journal	November 2023
Pothole detection in asphalt pavement images Koch, Christian; Brilakis, Ioannis Advanced Engineering Informatics, Vol. 25, Issue 3 https://doi.org/10.1016/j.aei.2011.01.002	journal	August 2011
A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure Koch, Christian; Georgieva, Kristina; Kasireddy, Varun Advanced Engineering Informatics, Vol. 29, Issue 2 https://doi.org/10.1016/j.aei.2015.01.008	journal	April 2015
Why 90% of clinical drug development fails and how to improve it? Sun, Duxin; Gao, Wei; Hu, Hongxiang Acta Pharmaceutica Sinica B, Vol. 12, Issue 7 https://doi.org/10.1016/j.apsb.2022.02.002	journal	July 2022
Machine learning meets continuous flow chemistry: Automated optimization towards the Pareto front of multiple objectives Schweidtmann, Artur M.; Clayton, Adam D.; Holmes, Nicholas Chemical Engineering Journal, Vol. 352 https://doi.org/10.1016/j.cej.2018.07.031	journal	November 2018
Automated self-optimisation of multi-step reaction and separation processes using machine learning Clayton, Adam D.; Schweidtmann, Artur M.; Clemens, Graeme Chemical Engineering Journal, Vol. 384 https://doi.org/10.1016/j.cej.2019.123340	journal	March 2020
Automated pH Adjustment Driven by Robotic Workflows and Active Machine Learning Pomberger, A.; Jose, N.; Walz, D. Chemical Engineering Journal, Vol. 451 https://doi.org/10.1016/j.cej.2022.139099	journal	January 2023
Exploring ultrafast flow chemistry by autonomous self-optimizing platform Ahn, Gwang-Noh; Kang, Ji-Ho; Lee, Hyune-Jea Chemical Engineering Journal, Vol. 453 https://doi.org/10.1016/j.cej.2022.139707	journal	February 2023
Robotised screening and characterisation for accelerated discovery of novel Lithium-ion battery electrolytes: Building a platform and proof of principle studies Svensson, Per H.; Yushmanov, Pavel; Tot, Aleksandar Chemical Engineering Journal, Vol. 455 https://doi.org/10.1016/j.cej.2022.140955 140955	journal	January 2023
A Deep Learning Approach to Antibiotic Discovery Stokes, Jonathan M.; Yang, Kevin; Swanson, Kyle Cell, Vol. 180, Issue 4 https://doi.org/10.1016/j.cell.2020.01.021	journal	February 2020
Fractional factorial design and simplex algorithm for optimizing sequential injection analysis (SIA) and second order calibration Pasamontes, Alberto; Callao, Pilar Chemometrics and Intelligent Laboratory Systems, Vol. 83, Issue 2 https://doi.org/10.1016/j.chemolab.2005.10.007	journal	September 2006
Automatic discovery and optimization of chemical processes Houben, Claudia; Lapkin, Alexei A. Current Opinion in Chemical Engineering, Vol. 9 https://doi.org/10.1016/j.coche.2015.07.001	journal	August 2015
Workflows in AiiDA: Engineering a high-throughput, event-based engine for robust and modular computational workflows Uhrin, Martin; Huber, Sebastiaan P.; Yu, Jusong Computational Materials Science, Vol. 187 https://doi.org/10.1016/j.commatsci.2020.110086	journal	February 2021
High-dimensional Bayesian optimization of 23 hyperparameters over 100 iterations for an attention-based network to predict materials property: A case study on CrabNet using Ax platform and SAASBO Baird, Sterling G.; Liu, Marianne; Sparks, Taylor D. Computational Materials Science, Vol. 211 https://doi.org/10.1016/j.commatsci.2022.111505	journal	August 2022
Compactness matters: Improving Bayesian optimization efficiency of materials formulations through invariant search spaces Baird, Sterling G.; Hall, Jason R.; Sparks, Taylor D. Computational Materials Science, Vol. 224 https://doi.org/10.1016/j.commatsci.2023.112134	journal	May 2023
Perspectives for self-driving labs in synthetic biology Martin, Hector G.; Radivojevic, Tijana; Zucker, Jeremy Current Opinion in Biotechnology, Vol. 79 https://doi.org/10.1016/j.copbio.2022.102881	journal	February 2023
DScribe: Library of descriptors for machine learning in materials science Himanen, Lauri; Jäger, Marc O. J.; Morooka, Eiaki V. Computer Physics Communications, Vol. 247 https://doi.org/10.1016/j.cpc.2019.106949	journal	February 2020
The Electrolab: An open-source, modular platform for automated characterization of redox-active electrolytes Oh, Inkyu; Pence, Michael A.; Lukhanin, Nikita G. Device, Vol. 1, Issue 5 https://doi.org/10.1016/j.device.2023.100103	journal	November 2023
PyLabRobot: An open-source, hardware-agnostic interface for liquid-handling robots and accessories Wierenga, Rick P.; Golas, Stefan M.; Ho, Wilson Device, Vol. 1, Issue 4 https://doi.org/10.1016/j.device.2023.100111	journal	October 2023
From eyes to cameras: Computer vision for high-throughput liquid-liquid separation El-khawaldeh, Rama; Mandal, Abhijoy; Yoshikawa, Naruki Device, Vol. 2, Issue 7 https://doi.org/10.1016/j.device.2024.100404	journal	July 2024
SMS-EMOA: Multiobjective selection based on dominated hypervolume Beume, Nicola; Naujoks, Boris; Emmerich, Michael European Journal of Operational Research, Vol. 181, Issue 3 https://doi.org/10.1016/j.ejor.2006.08.008	journal	September 2007
Tuning and high throughput experimental screening of eutectic electrolytes with co-solvents for redox flow batteries Dean, William; Muñoz, Miguel; Noh, Juran Electrochimica Acta, Vol. 474, p. 143517 https://doi.org/10.1016/j.electacta.2023.143517	journal	December 2023
Automated high-throughput preparation and characterization of oligonucleotide-loaded lipid nanoparticles Fan, Yuchen; Yen, Chun-Wan; Lin, Hsiu-Chao International Journal of Pharmaceutics, Vol. 599 https://doi.org/10.1016/j.ijpharm.2021.120392	journal	April 2021
Automated solubility screening platform using computer vision Shiri, Parisa; Lai, Veronica; Zepel, Tara iScience, Vol. 24, Issue 3 https://doi.org/10.1016/j.isci.2021.102176	journal	March 2021
Using simulation to accelerate autonomous experimentation: A case study using mechanics Gongora, Aldair E.; Snapp, Kelsey L.; Whiting, Emily iScience, Vol. 24, Issue 4 https://doi.org/10.1016/j.isci.2021.102262	journal	April 2021
Performance of Search Algorithms in the Examination of Chemical Reaction Spaces with an Automated Chemistry Workstation Lindsey, James M. Dixon and Jonathan S. JALA: Journal of the Association for Laboratory Automation, Vol. 9, Issue 6 https://doi.org/10.1016/j.jala.2004.08.004	journal	December 2004
Optimisation of olefin epoxidation catalysts with the application of high-throughput and genetic algorithms assisted by artificial neural networks (softcomputing techniques) Corma, A.; Serra, J.; Serna, P. Journal of Catalysis, Vol. 229, Issue 2 https://doi.org/10.1016/j.jcat.2004.11.024	journal	January 2005
Data driven design of alkali-activated concrete using sequential learning Völker, Christoph; Moreno Torres, Benjami; Rug, Tehseen Journal of Cleaner Production, Vol. 418 https://doi.org/10.1016/j.jclepro.2023.138221	journal	September 2023
Innovation in the pharmaceutical industry: New estimates of R&D costs DiMasi, Joseph A.; Grabowski, Henry G.; Hansen, Ronald W. Journal of Health Economics, Vol. 47 https://doi.org/10.1016/j.jhealeco.2016.01.012	journal	May 2016
High-throughput characterization methods for lithium batteries Lyu, Yingchun; Liu, Yali; Cheng, Tao Journal of Materiomics, Vol. 3, Issue 3 https://doi.org/10.1016/j.jmat.2017.08.001	journal	September 2017
Single-Junction Organic Solar Cell with over 15% Efficiency Using Fused-Ring Acceptor with Electron-Deficient Core Yuan, Jun; Zhang, Yunqiang; Zhou, Liuyang Joule, Vol. 3, Issue 4 https://doi.org/10.1016/j.joule.2019.01.004	journal	April 2019
Electrolytic Conversion of Bicarbonate into CO in a Flow Cell Li, Tengfei; Lees, Eric W.; Goldman, Maxwell Joule, Vol. 3, Issue 6 https://doi.org/10.1016/j.joule.2019.05.021	journal	June 2019
Chelated Chromium Electrolyte Enabling High-Voltage Aqueous Flow Batteries Robb, Brian H.; Farrell, Jason M.; Marshak, Michael P. Joule, Vol. 3, Issue 10 https://doi.org/10.1016/j.joule.2019.07.002	journal	October 2019
Rapid Open-Air Fabrication of Perovskite Solar Modules Rolston, Nicholas; Scheideler, William J.; Flick, Austin C. Joule, Vol. 4, Issue 12 https://doi.org/10.1016/j.joule.2020.11.001	journal	December 2020
Elucidating the Full Potential of OPV Materials Utilizing a High-Throughput Robot-Based Platform and Machine Learning Du, Xiaoyan; Lüer, Larry; Heumueller, Thomas Joule, Vol. 5, Issue 2 https://doi.org/10.1016/j.joule.2020.12.013	journal	February 2021
Machine learning for high-throughput experimental exploration of metal halide perovskites Ahmadi, Mahshid; Ziatdinov, Maxim; Zhou, Yuanyuan Joule, Vol. 5, Issue 11 https://doi.org/10.1016/j.joule.2021.10.001	journal	November 2021
Machine learning with knowledge constraints for process optimization of open-air perovskite solar cell manufacturing Liu, Zhe; Rolston, Nicholas; Flick, Austin C. Joule, Vol. 6, Issue 4 https://doi.org/10.1016/j.joule.2022.03.003	journal	April 2022
Review on microfabricated micro-solid oxide fuel cell membranes Evans, Anna; Bieberle-Hütter, Anja; Rupp, Jennifer L. M. Journal of Power Sources, Vol. 194, Issue 1 https://doi.org/10.1016/j.jpowsour.2009.03.048	journal	October 2009
Machine-Learning-Accelerated Perovskite Crystallization Kirman, Jeffrey; Johnston, Andrew; Kuntz, Douglas A. Matter, Vol. 2, Issue 4 https://doi.org/10.1016/j.matt.2020.02.012	journal	April 2020
Scanning probes as a materials automation platform with extremely miniaturized samples Brown, Keith A. Matter, Vol. 5, Issue 10 https://doi.org/10.1016/j.matt.2022.08.004	journal	October 2022
An object-oriented framework to enable workflow evolution across materials acceleration platforms Leong, Chang Jie; Low, Kai Yuan Andre; Recatala-Gomez, Jose Matter, Vol. 5, Issue 10 https://doi.org/10.1016/j.matt.2022.08.017	journal	October 2022
Self-driving laboratories: A paradigm shift in nanomedicine development Hickman, Riley J.; Bannigan, Pauric; Bao, Zeqing Matter, Vol. 6, Issue 4 https://doi.org/10.1016/j.matt.2023.02.007	journal	April 2023
Scalable multi-agent lab framework for lab optimization Kusne, A. Gilad; McDannald, Austin Matter, Vol. 6, Issue 6 https://doi.org/10.1016/j.matt.2023.03.022	journal	June 2023
Sustainable materials acceleration platform reveals stable and efficient wide-bandgap metal halide perovskite alloys Wang, Tonghui; Li, Ruipeng; Ardekani, Hossein Matter, Vol. 6, Issue 9 https://doi.org/10.1016/j.matt.2023.06.040	journal	September 2023
Brokering between tenants for an international materials acceleration platform Vogler, Monika; Busk, Jonas; Hajiyani, Hamidreza Matter, Vol. 6, Issue 9 https://doi.org/10.1016/j.matt.2023.07.016	journal	September 2023
ChemOS 2.0: An orchestration architecture for chemical self-driving laboratories Sim, Malcolm; Vakili, Mohammad Ghazi; Strieth-Kalthoff, Felix Matter https://doi.org/10.1016/j.matt.2024.04.022	journal	May 2024
Simplex optimization: A tutorial approach and recent applications in analytical chemistry Bezerra, Marcos Almeida; dos Santos, Queila Oliveira; Santos, Aldenor Gomes Microchemical Journal, Vol. 124 https://doi.org/10.1016/j.microc.2015.07.023	journal	January 2016
Application of artificial neural networks to high-throughput synthesis of zeolites Moliner, M.; Serra, J. M.; Corma, A. Microporous and Mesoporous Materials, Vol. 78, Issue 1 https://doi.org/10.1016/j.micromeso.2004.09.018	journal	February 2005
Sidekick: A Low-Cost Open-Source 3D-printed liquid dispensing robot Keesey, Rodolfo; LeSuer, Robert; Schrier, Joshua HardwareX, Vol. 12 https://doi.org/10.1016/j.ohx.2022.e00319	journal	October 2022
SELFIES and the future of molecular string representations Krenn, Mario; Ai, Qianxiang; Barthel, Senja Patterns, Vol. 3, Issue 10 https://doi.org/10.1016/j.patter.2022.100588	journal	October 2022
Autonomous scanning probe microscopy with hypothesis learning: Exploring the physics of domain switching in ferroelectric materials Liu, Yongtao; Morozovska, Anna N.; Eliseev, Eugene A. Patterns, Vol. 4, Issue 3 https://doi.org/10.1016/j.patter.2023.100704	journal	March 2023
Enzyme assays Bisswanger, Hans Perspectives in Science, Vol. 1, Issue 1-6 https://doi.org/10.1016/j.pisc.2014.02.005	journal	May 2014
Radical addition–fragmentation chemistry in polymer synthesis Moad, Graeme; Rizzardo, Ezio; Thang, San H. Polymer, Vol. 49, Issue 5 https://doi.org/10.1016/j.polymer.2007.11.020	journal	March 2008
Hydrogen energy systems: A critical review of technologies, applications, trends and challenges Yue, Meiling; Lambert, Hugo; Pahon, Elodie Renewable and Sustainable Energy Reviews, Vol. 146 https://doi.org/10.1016/j.rser.2021.111180	journal	August 2021
New strategies and tests to accelerate discovery and development of multi-principal element structural alloys Miracle, Daniel; Majumdar, Bhaskar; Wertz, Katelun Scripta Materialia, Vol. 127 https://doi.org/10.1016/j.scriptamat.2016.08.001	journal	January 2017
The SiLA 2 Manager for rapid device integration and workflow automation Bromig, Lukas; Leiter, David; Mardale, Alexandru-Virgil SoftwareX, Vol. 17 https://doi.org/10.1016/j.softx.2022.100991	journal	January 2022
Flow-injection chemiluminescence determination of formaldehyde in water Motyka, K.; Onjia, A.; Mikuska, P. Talanta, Vol. 71, Issue 2 https://doi.org/10.1016/j.talanta.2006.05.078	journal	February 2007
MultiSimplex optimization of on-line sorbent proconcentration and determination of iron by FI-AAS and microcolumn of immobilized ferron Shakerian, F.; Dadfarnia, S.; Shabani, A. Talanta, Vol. 77, Issue 2 https://doi.org/10.1016/j.talanta.2008.03.015	journal	December 2008
Enhanced process development using automated continuous reactors by self-optimisation algorithms and statistical empirical modelling Jeraal, Mohammed I.; Holmes, Nicholas; Akien, Geoffrey R. Tetrahedron, Vol. 74, Issue 25 https://doi.org/10.1016/j.tet.2018.02.061	journal	June 2018
AI-powered therapeutic target discovery Pun, Frank W.; Ozerov, Ivan V.; Zhavoronkov, Alex Trends in Pharmacological Sciences, Vol. 44, Issue 9 https://doi.org/10.1016/j.tips.2023.06.010	journal	September 2023
Next-Generation Experimentation with Self-Driving Laboratories Häse, Florian; Roch, Loïc M.; Aspuru-Guzik, Alán Trends in Chemistry, Vol. 1, Issue 3 https://doi.org/10.1016/j.trechm.2019.02.007	journal	June 2019
Toward Machine Learning-Enhanced High-Throughput Experimentation Eyke, Natalie S.; Koscher, Brent A.; Jensen, Klavs F. Trends in Chemistry, Vol. 3, Issue 2 https://doi.org/10.1016/j.trechm.2020.12.001	journal	February 2021
When machine learning meets molecular synthesis Oliveira, João C. A.; Frey, Johanna; Zhang, Shuo-Qing Trends in Chemistry, Vol. 4, Issue 10 https://doi.org/10.1016/j.trechm.2022.07.005	journal	October 2022
Balancing act: when to flex and when to stay fixed El-khawaldeh, Rama; Hein, Jason E. Trends in Chemistry, Vol. 6, Issue 1 https://doi.org/10.1016/j.trechm.2023.10.008	journal	January 2024
Autonomous Discovery of Battery Electrolytes with Robotic Experimentation and Machine Learning Dave, Adarsh; Mitchell, Jared; Kandasamy, Kirthevasan Cell Reports Physical Science, Vol. 1, Issue 12 https://doi.org/10.1016/j.xcrp.2020.100264	journal	December 2020
Optimization of Formulations Using Robotic Experiments Driven by Machine Learning DoE Cao, Liwei; Russo, Danilo; Felton, Kobi Cell Reports Physical Science, Vol. 2, Issue 1 https://doi.org/10.1016/j.xcrp.2020.100295	journal	January 2021
Data-driven automated robotic experiments accelerate discovery of multi-component electrolyte for rechargeable Li–O2 batteries Matsuda, Shoichi; Lambard, Guillaume; Sodeyama, Keitaro Cell Reports Physical Science, Vol. 3, Issue 4 https://doi.org/10.1016/j.xcrp.2022.100832	journal	April 2022
Biologic formulation in a self-driving biomaterials lab Tamasi, Matthew J.; Gormley, Adam J. Cell Reports Physical Science, Vol. 3, Issue 9 https://doi.org/10.1016/j.xcrp.2022.101041	journal	September 2022
High-throughput solubility determination for data-driven materials design and discovery in redox flow battery research Liang, Yangang; Job, Heather; Feng, Ruozhu Cell Reports Physical Science, Vol. 4, Issue 10 https://doi.org/10.1016/j.xcrp.2023.101633	journal	October 2023
An Introduction to Multivariate Calibration and Analysis Beebe, Kenneth R.; Kowalski, Bruce R. Analytical Chemistry, Vol. 59, Issue 17 https://doi.org/10.1021/ac00144a725	journal	September 1987
Robotic sample preparation station Owens, Grover D.; Eckstein, Rodney J. Analytical Chemistry, Vol. 54, Issue 13 https://doi.org/10.1021/ac00250a047	journal	November 1982
Closed-Loop, Multiobjective Optimization of Analytical Instrumentation: Gas Chromatography/Time-of-Flight Mass Spectrometry of the Metabolomes of Human Serum and of Yeast Fermentations O'Hagan, Steve; Dunn, Warwick B.; Brown, Marie Analytical Chemistry, Vol. 77, Issue 1 https://doi.org/10.1021/ac049146x	journal	December 2004
Closed-Loop, Multiobjective Optimization of Two-Dimensional Gas Chromatography/Mass Spectrometry for Serum Metabolomics Analytical Chemistry, Vol. 79, Issue 2 https://doi.org/10.1021/ac061443+	journal	December 2006
Fully automated stopped-flow studies with a hierarchical computer controlled system Mieling, Glen E.; Taylor, Richard W.; Hargis, Larry G. Analytical Chemistry, Vol. 48, Issue 12 https://doi.org/10.1021/ac50006a015	journal	October 1976
Simplex algorithm for the optimization of gradient elution high-performance liquid chromatography Watson, Mark W.; Carr, Peter W. Analytical Chemistry, Vol. 51, Issue 11 https://doi.org/10.1021/ac50047a052	journal	September 1979
Instrument for automated synthesis of peptides Merrifield, Robert B.; Stewart, John Morrow.; Jernberg, Nils. Analytical Chemistry, Vol. 38, Issue 13 https://doi.org/10.1021/ac50155a057	journal	December 1966
Design of Experiments in Industrial Research Smallwood, H. M. Analytical Chemistry, Vol. 19, Issue 12 https://doi.org/10.1021/ac60012a005	journal	December 1947
Simplex optimization of analytical chemical methods Morgan, Stephen L.; Deming, Stanley N. Analytical Chemistry, Vol. 46, Issue 9 https://doi.org/10.1021/ac60345a035	journal	August 1974
UNIPLEX. Single-factor optimization of response in the presence of error King, Paul G.; Deming, Stanley N. Analytical Chemistry, Vol. 46, Issue 11 https://doi.org/10.1021/ac60347a009	journal	September 1974
Development of a Robust and Repeatable UPLC−MS Method for the Long-Term Metabolomic Study of Human Serum Zelena, Eva; Dunn, Warwick B.; Broadhurst, David Analytical Chemistry, Vol. 81, Issue 4 https://doi.org/10.1021/ac8019366	journal	January 2009
Applications of Deep Learning in Molecule Generation and Molecular Property Prediction Walters, W. Patrick; Barzilay, Regina Accounts of Chemical Research, Vol. 54, Issue 2 https://doi.org/10.1021/acs.accounts.0c00699	journal	December 2020
Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab Seifrid, Martin; Pollice, Robert; Aguilar-Granda, Andrés Accounts of Chemical Research, Vol. 55, Issue 17 https://doi.org/10.1021/acs.accounts.2c00220	journal	August 2022
Feedback in Flow for Accelerated Reaction Development Reizman, Brandon J.; Jensen, Klavs F. Accounts of Chemical Research, Vol. 49, Issue 9 https://doi.org/10.1021/acs.accounts.6b00261	journal	August 2016
Machine Learning Applied to Zeolite Synthesis: The Missing Link for Realizing High-Throughput Discovery Moliner, Manuel; Román-Leshkov, Yuriy; Corma, Avelino Accounts of Chemical Research, Vol. 52, Issue 10 https://doi.org/10.1021/acs.accounts.9b00399	journal	September 2019
Robot-Accelerated Perovskite Investigation and Discovery Li, Zhi; Najeeb, Mansoor Ani; Alves, Liana Chemistry of Materials, Vol. 32, Issue 13 https://doi.org/10.1021/acs.chemmater.0c01153	journal	June 2020
Self-Driving Laboratory for Polymer Electronics Vriza, Aikaterini; Chan, Henry; Xu, Jie Chemistry of Materials, Vol. 35, Issue 8 https://doi.org/10.1021/acs.chemmater.2c03593	journal	March 2023
Big-Data Science in Porous Materials: Materials Genomics and Machine Learning Jablonka, Kevin Maik; Ongari, Daniele; Moosavi, Seyed Mohamad Chemical Reviews, Vol. 120, Issue 16 https://doi.org/10.1021/acs.chemrev.0c00004	journal	June 2020
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems Keith, John A.; Vassilev-Galindo, Valentin; Cheng, Bingqing Chemical Reviews, Vol. 121, Issue 16 https://doi.org/10.1021/acs.chemrev.1c00107	journal	July 2021
Artificial Intelligence Applied to Battery Research: Hype or Reality? Lombardo, Teo; Duquesnoy, Marc; El-Bouysidy, Hassna Chemical Reviews https://doi.org/10.1021/acs.chemrev.1c00108	journal	September 2021
A Brief Introduction to Chemical Reaction Optimization Taylor, Connor J.; Pomberger, Alexander; Felton, Kobi C. Chemical Reviews, Vol. 123, Issue 6 https://doi.org/10.1021/acs.chemrev.2c00798	journal	February 2023
The Hitchhiker’s Guide to Flow Chemistry Plutschack, Matthew B.; Pieber, Bartholomäus; Gilmore, Kerry Chemical Reviews, Vol. 117, Issue 18 https://doi.org/10.1021/acs.chemrev.7b00183	journal	June 2017
Lithium–Oxygen Batteries and Related Systems: Potential, Status, and Future Kwak, Won-Jin; Sharon, Daniel Chemical Reviews, Vol. 120, Issue 14, p. 6626-6683 https://doi.org/10.1021/acs.chemrev.9b00609	journal	March 2020
MOFX-DB: An Online Database of Computational Adsorption Data for Nanoporous Materials Bobbitt, N. Scott; Shi, Kaihang; Bucior, Benjamin J. Journal of Chemical & Engineering Data, Vol. 68, Issue 2 https://doi.org/10.1021/acs.jced.2c00583	journal	January 2023
REINVENT 2.0: An AI Tool for De Novo Drug Design Blaschke, Thomas; Arús-Pous, Josep; Chen, Hongming Journal of Chemical Information and Modeling, Vol. 60, Issue 12 https://doi.org/10.1021/acs.jcim.0c00915	journal	October 2020
Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation Altintas, Cigdem; Altundal, Omer Faruk; Keskin, Seda Journal of Chemical Information and Modeling, Vol. 61, Issue 5 https://doi.org/10.1021/acs.jcim.1c00191	journal	April 2021
Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance Sharma, Vidushi; Giammona, Maxwell; Zubarev, Dmitry Journal of Chemical Information and Modeling, Vol. 63, Issue 22 https://doi.org/10.1021/acs.jcim.3c01030	journal	November 2023
GuacaMol: Benchmarking Models for de Novo Molecular Design Brown, Nathan; Fiscato, Marco; Segler, Marwin H. S. Journal of Chemical Information and Modeling, Vol. 59, Issue 3 https://doi.org/10.1021/acs.jcim.8b00839	journal	October 2018
Analyzing Learned Molecular Representations for Property Prediction Yang, Kevin; Swanson, Kyle; Jin, Wengong Journal of Chemical Information and Modeling, Vol. 59, Issue 8 https://doi.org/10.1021/acs.jcim.9b00237	journal	July 2019
QMflows: A Tool Kit for Interoperable Parallel Workflows in Quantum Chemistry Zapata, Felipe; Ridder, Lars; Hidding, Johan Journal of Chemical Information and Modeling, Vol. 59, Issue 7 https://doi.org/10.1021/acs.jcim.9b00384	journal	June 2019
ReNeGate: A Reaction Network Graph-Theoretical Tool for Automated Mechanistic Studies in Computational Homogeneous Catalysis Hashemi, Ali; Bougueroua, Sana; Gaigeot, Marie-Pierre Journal of Chemical Theory and Computation, Vol. 18, Issue 12 https://doi.org/10.1021/acs.jctc.2c00404	journal	November 2022
The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design Alford, Rebecca F.; Leaver-Fay, Andrew; Jeliazkov, Jeliazko R. Journal of Chemical Theory and Computation, Vol. 13, Issue 6 https://doi.org/10.1021/acs.jctc.7b00125	journal	May 2017
AARON: An Automated Reaction Optimizer for New Catalysts Guan, Yanfei; Ingman, Victoria M.; Rooks, Benjamin J. Journal of Chemical Theory and Computation, Vol. 14, Issue 10 https://doi.org/10.1021/acs.jctc.8b00578	journal	August 2018
MicroCycle: An Integrated and Automated Platform to Accelerate Drug Discovery Brocklehurst, Cara E.; Altmann, Eva; Bon, Corentin Journal of Medicinal Chemistry, Vol. 67, Issue 3 https://doi.org/10.1021/acs.jmedchem.3c02029	journal	January 2024
An Autonomous Self-Optimizing Flow Reactor for the Synthesis of Natural Product Carpanone Cortés-Borda, Daniel; Wimmer, Eric; Gouilleux, Boris The Journal of Organic Chemistry, Vol. 83, Issue 23 https://doi.org/10.1021/acs.joc.8b01821	journal	September 2018
Vision-Guided Automation Platform for Liquid–Liquid Extraction and Workup Development Sun, Alexandra C.; Jurica, Jon A.; Rose, Harrison B. Organic Process Research & Development, Vol. 27, Issue 11 https://doi.org/10.1021/acs.oprd.3c00217	journal	October 2023
Closed-Loop Multitarget Optimization for Discovery of New Emulsion Polymerization Recipes Houben, Claudia; Peremezhney, Nicolai; Zubov, Alexandr Organic Process Research & Development, Vol. 19, Issue 8 https://doi.org/10.1021/acs.oprd.5b00210	journal	July 2015
A Novel Internet-Based Reaction Monitoring, Control and Autonomous Self-Optimization Platform for Chemical Synthesis Fitzpatrick, Daniel E.; Battilocchio, Claudio; Ley, Steven V. Organic Process Research & Development, Vol. 20, Issue 2 https://doi.org/10.1021/acs.oprd.5b00313	journal	March 2015
Enabling the Scale-Up of a Key Asymmetric Hydrogenation Step in the Synthesis of an API Using Continuous Flow Solid-Supported Catalysis Amara, Zacharias; Poliakoff, Martyn; Duque, Rubén Organic Process Research & Development, Vol. 20, Issue 7 https://doi.org/10.1021/acs.oprd.6b00143	journal	June 2016
Optimizing the Heck–Matsuda Reaction in Flow with a Constraint-Adapted Direct Search Algorithm Cortés-Borda, Daniel; Kutonova, Ksenia V.; Jamet, Corentin Organic Process Research & Development, Vol. 20, Issue 11 https://doi.org/10.1021/acs.oprd.6b00310	journal	October 2016
Photoredox Iridium–Nickel Dual-Catalyzed Decarboxylative Arylation Cross-Coupling: From Batch to Continuous Flow via Self-Optimizing Segmented Flow Reactor Hsieh, Hsiao-Wu; Coley, Connor W.; Baumgartner, Lorenz M. Organic Process Research & Development, Vol. 22, Issue 4 https://doi.org/10.1021/acs.oprd.8b00018	journal	July 2016
Accelerate Synthesis of Metal–Organic Frameworks by a Robotic Platform and Bayesian Optimization Xie, Yunchao; Zhang, Chi; Deng, Heng ACS Applied Materials & Interfaces, Vol. 13, Issue 45 https://doi.org/10.1021/acsami.1c16506	journal	October 2021
Robot-Based High-Throughput Engineering of Alcoholic Polymer: Fullerene Nanoparticle Inks for an Eco-Friendly Processing of Organic Solar Cells Xie, Chen; Tang, Xiaofeng; Berlinghof, Marvin ACS Applied Materials & Interfaces, Vol. 10, Issue 27 https://doi.org/10.1021/acsami.8b03621	journal	June 2018
Using an Automated Monitoring Platform for Investigations of Biphasic Reactions Daponte, Jordan A.; Guo, Yuejun; Ruck, Rebecca T. ACS Catalysis, Vol. 9, Issue 12 https://doi.org/10.1021/acscatal.9b03953	journal	November 2019
Machine Learning for Catalysis Informatics: Recent Applications and Prospects Toyao, Takashi; Maeno, Zen; Takakusagi, Satoru ACS Catalysis, Vol. 10, Issue 3 https://doi.org/10.1021/acscatal.9b04186	journal	December 2019
A Perspective toward Practical Lithium–Sulfur Batteries Zhao, Meng; Li, Bo-Quan; Zhang, Xue-Qiang ACS Central Science, Vol. 6, Issue 7 https://doi.org/10.1021/acscentsci.0c00449	journal	June 2020
Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Data Set Eppel, Sagi; Xu, Haoping; Bismuth, Mor ACS Central Science, Vol. 6, Issue 10 https://doi.org/10.1021/acscentsci.0c00460	journal	September 2020
Discovering New Chemistry with an Autonomous Robotic Platform Driven by a Reactivity-Seeking Neural Network Caramelli, Dario; Granda, Jarosław M.; Mehr, S. Hessam M. ACS Central Science, Vol. 7, Issue 11 https://doi.org/10.1021/acscentsci.1c00435	journal	November 2021
Bayesian Optimization of Computer-Proposed Multistep Synthetic Routes on an Automated Robotic Flow Platform Nambiar, Anirudh M. K.; Breen, Christopher P.; Hart, Travis ACS Central Science, Vol. 8, Issue 6 https://doi.org/10.1021/acscentsci.2c00207	journal	June 2022
Robotic Modules for the Programmable Chemputation of Molecules and Materials Salley, Daniel; Manzano, J. Sebastián; Kitson, Philip J. ACS Central Science, Vol. 9, Issue 8 https://doi.org/10.1021/acscentsci.3c00304	journal	July 2023
ChatGPT Research Group for Optimizing the Crystallinity of MOFs and COFs Zheng, Zhiling; Zhang, Oufan; Nguyen, Ha L. ACS Central Science, Vol. 9, Issue 11, p. 2161-2170 https://doi.org/10.1021/acscentsci.3c01087	journal	November 2023
Optimizing Chemical Reactions with Deep Reinforcement Learning Zhou, Zhenpeng; Li, Xiaocheng; Zare, Richard N. ACS Central Science, Vol. 3, Issue 12 https://doi.org/10.1021/acscentsci.7b00492	journal	November 2017
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules Gómez-Bombarelli, Rafael; Wei, Jennifer N.; Duvenaud, David ACS Central Science, Vol. 4, Issue 2 https://doi.org/10.1021/acscentsci.7b00572	journal	January 2018
Phoenics: A Bayesian Optimizer for Chemistry Häse, Florian; Roch, Loïc M.; Kreisbeck, Christoph ACS Central Science, Vol. 4, Issue 9 https://doi.org/10.1021/acscentsci.8b00307	journal	August 2018
A Mixed Quantum Chemistry/Machine Learning Approach for the Fast and Accurate Prediction of Biochemical Redox Potentials and Its Large-Scale Application to 315 000 Redox Reactions Jinich, Adrian; Sanchez-Lengeling, Benjamin; Ren, Haniu ACS Central Science, Vol. 5, Issue 7 https://doi.org/10.1021/acscentsci.9b00297	journal	June 2019
An Integrated Microfluidic Processor for DNA-Encoded Combinatorial Library Functional Screening MacConnell, Andrew B.; Price, Alexander K.; Paegel, Brian M. ACS Combinatorial Science, Vol. 19, Issue 3 https://doi.org/10.1021/acscombsci.6b00192	journal	February 2017
Machine Learning Using Combined Structural and Chemical Descriptors for Prediction of Methane Adsorption Performance of Metal Organic Frameworks (MOFs) Pardakhti, Maryam; Moharreri, Ehsan; Wanik, David ACS Combinatorial Science, Vol. 19, Issue 10 https://doi.org/10.1021/acscombsci.7b00056	journal	September 2017
Chemical Robotics Enabled Exploration of Stability in Multicomponent Lead Halide Perovskites via Machine Learning Higgins, Kate; Valleti, Sai Mani; Ziatdinov, Maxim ACS Energy Letters, Vol. 5, Issue 11 https://doi.org/10.1021/acsenergylett.0c01749	journal	October 2020
How Machine Learning Will Revolutionize Electrochemical Sciences Mistry, Aashutosh; Franco, Alejandro A.; Cooper, Samuel J. ACS Energy Letters https://doi.org/10.1021/acsenergylett.1c00194	journal	March 2021
Experimental Protocols for Studying Organic Non-aqueous Redox Flow Batteries Li, Min; Odom, Susan A.; Pancoast, Adam R. ACS Energy Letters, Vol. 6, Issue 11 https://doi.org/10.1021/acsenergylett.1c01675	journal	October 2021
Electrochemical Mechanistic Analysis from Cyclic Voltammograms Based on Deep Learning Hoar, Benjamin B.; Zhang, Weitong; Xu, Shuangning ACS Measurement Science Au, Vol. 2, Issue 6 https://doi.org/10.1021/acsmeasuresciau.2c00045	journal	August 2022
Bayesian Active Learning for Scanning Probe Microscopy: From Gaussian Processes to Hypothesis Learning Ziatdinov, Maxim; Liu, Yongtao; Kelley, Kyle ACS Nano, Vol. 16, Issue 9 https://doi.org/10.1021/acsnano.2c05303	journal	September 2022
How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics Cao, Bing; Adutwum, Lawrence A.; Oliynyk, Anton O. ACS Nano, Vol. 12, Issue 8 https://doi.org/10.1021/acsnano.8b04726	journal	July 2018
Review on Metal–Organic Framework Classification, Synthetic Approaches, and Influencing Factors: Applications in Energy, Drug Delivery, and Wastewater Treatment Yusuf, Vadia Foziya; Malek, Naved I.; Kailasa, Suresh Kumar ACS Omega, Vol. 7, Issue 49 https://doi.org/10.1021/acsomega.2c05310	journal	December 2022
Fully Automated One-Step Synthesis of Single-Transcript TALEN Pairs Using a Biological Foundry Chao, Ran; Liang, Jing; Tasan, Ipek ACS Synthetic Biology, Vol. 6, Issue 4 https://doi.org/10.1021/acssynbio.6b00293	journal	January 2017
Opportunities at the Intersection of Synthetic Biology, Machine Learning, and Automation Carbonell, Pablo; Radivojevic, Tijana; García Martín, Héctor ACS Synthetic Biology, Vol. 8, Issue 7 https://doi.org/10.1021/acssynbio.8b00540	journal	July 2019
The Generation of a Unique Machine Description for Chemical Structures-A Technique Developed at Chemical Abstracts Service. Morgan, H. L. Journal of Chemical Documentation, Vol. 5, Issue 2 https://doi.org/10.1021/c160017a018	journal	May 1965
Robotics In The Laboratory Isenhour, T. L. Journal of Chemical Information and Computer Sciences, Vol. 25, Issue 3 https://doi.org/10.1021/ci00047a600	journal	August 1985
SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules Weininger, David Journal of Chemical Information and Modeling, Vol. 28, Issue 1 https://doi.org/10.1021/ci00057a005	journal	February 1988
Extended-Connectivity Fingerprints Rogers, David; Hahn, Mathew Journal of Chemical Information and Modeling, Vol. 50, Issue 5 https://doi.org/10.1021/ci100050t	journal	April 2010
Managing the Computational Chemistry Big Data Problem: The ioChem-BD Platform Álvarez-Moreno, M.; de Graaf, C.; López, N. Journal of Chemical Information and Modeling, Vol. 55, Issue 1 https://doi.org/10.1021/ci500593j	journal	December 2014
Preparation and Growth Mechanism of Gold Nanorods (NRs) Using Seed-Mediated Growth Method Nikoobakht, Babak; El-Sayed, Mostafa A. Chemistry of Materials, Vol. 15, Issue 10 https://doi.org/10.1021/cm020732l	journal	May 2003
A New Mapping/Exploration Approach for HT Synthesis of Zeolites Corma, Avelino; Moliner, Manuel; Serra, Jose M. Chemistry of Materials, Vol. 18, Issue 14 https://doi.org/10.1021/cm060620k	journal	July 2006
Optimization with Multiple Performance Criteria. Application to Minimization of Parameter Sensitivlties in a Refinery Model Seinfeld, John H.; McBride, Warren L. Industrial & Engineering Chemistry Process Design and Development, Vol. 9, Issue 1 https://doi.org/10.1021/i260033a010	journal	January 1970
Use of random admissible values for control in iterative dynamic programming Bojkov, Bojan; Luus, Rein Industrial & Engineering Chemistry Research, Vol. 31, Issue 5 https://doi.org/10.1021/ie00005a011	journal	May 1992
Closed-Loop Composition and Molecular Weight Control of a Copolymer Latex Using Near-Infrared Spectroscopy Vieira, Raphael A. M.; Sayer, Claudia; Lima, Enrique L. Industrial & Engineering Chemistry Research, Vol. 41, Issue 12 https://doi.org/10.1021/ie0103557	journal	May 2002
Laboratory automation. 1: syntheses via vinyl sulfones. 14. Robotic orchestration of organic reactions: yield optimization via an automated system with operator-specified reaction sequences Frisbee, A. R.; Nantz, M. H.; Kramer, G. W. Journal of the American Chemical Society, Vol. 106, Issue 23 https://doi.org/10.1021/ja00335a047	journal	November 1984
Complex Ions. I. The Identification of Complex Ions in Solution by Spectrophotometric Measurements Vosburgh, Warren C.; Cooper, Gerald R. Journal of the American Chemical Society, Vol. 63, Issue 2 https://doi.org/10.1021/ja01847a025	journal	February 1941
Evolution of Catalysts Directed by Genetic Algorithms in a Plug-Based Microfluidic Device Tested with Oxidation of Methane by Oxygen Kreutz, Jason E.; Shukhaev, Anton; Du, Wenbin Journal of the American Chemical Society, Vol. 132, Issue 9 https://doi.org/10.1021/ja909853x	journal	February 2010
The Open Reaction Database Kearnes, Steven M.; Maser, Michael R.; Wleklinski, Michael Journal of the American Chemical Society, Vol. 143, Issue 45 https://doi.org/10.1021/jacs.1c09820	journal	November 2021
A Multi-Objective Active Learning Platform and Web App for Reaction Optimization Torres, Jose Antonio Garrido; Lau, Sii Hong; Anchuri, Pranay Journal of the American Chemical Society, Vol. 144, Issue 43 https://doi.org/10.1021/jacs.2c08592	journal	October 2022
Machine Learning Assisted Synthesis of Metal–Organic Nanocapsules Xie, Yunchao; Zhang, Chen; Hu, Xiangquan Journal of the American Chemical Society, Vol. 142, Issue 3 https://doi.org/10.1021/jacs.9b11569	journal	December 2019
From Platform to Knowledge Graph: Evolution of Laboratory Automation Bai, Jiaru; Cao, Liwei; Mosbach, Sebastian JACS Au, Vol. 2, Issue 2 https://doi.org/10.1021/jacsau.1c00438	journal	January 2022
Rapid Discovery of a Novel Series of Abl Kinase Inhibitors by Application of an Integrated Microfluidic Synthesis and Screening Platform Desai, Bimbisar; Dixon, Karen; Farrant, Elizabeth Journal of Medicinal Chemistry, Vol. 56, Issue 7 https://doi.org/10.1021/jm400099d	journal	March 2013
The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid Hachmann, Johannes; Olivares-Amaya, Roberto; Atahan-Evrenk, Sule The Journal of Physical Chemistry Letters, Vol. 2, Issue 17 https://doi.org/10.1021/jz200866s	journal	August 2011
Accelerating Electrolyte Discovery for Energy Storage with High-Throughput Screening Cheng, Lei; Assary, Rajeev S.; Qu, Xiaohui The Journal of Physical Chemistry Letters, Vol. 6, Issue 2 https://doi.org/10.1021/jz502319n	journal	January 2015
Integrated Synthesis and Testing of Substituted Xanthine Based DPP4 Inhibitors: Application to Drug Discovery Czechtizky, Werngard; Dedio, Jüergen; Desai, Bimbisar ACS Medicinal Chemistry Letters, Vol. 4, Issue 8 https://doi.org/10.1021/ml400171b	journal	June 2013
Discovery of Wall-Selective Carbon Nanotube Growth Conditions via Automated Experimentation Nikolaev, Pavel; Hooper, Daylond; Perea-López, Nestor ACS Nano, Vol. 8, Issue 10 https://doi.org/10.1021/nn503347a	journal	October 2014
An Automated Microfluidic System for Online Optimization in Chemical Synthesis McMullen, Jonathan P.; Jensen, Klavs F. Organic Process Research & Development, Vol. 14, Issue 5 https://doi.org/10.1021/op100123e	journal	September 2010
Rapid Determination of Reaction Kinetics with an Automated Microfluidic System McMullen, Jonathan P.; Jensen, Klavs F. Organic Process Research & Development, Vol. 15, Issue 2 https://doi.org/10.1021/op100300p	journal	March 2011
Adaptive Process Optimization for Continuous Methylation of Alcohols in Supercritical Carbon Dioxide Bourne, Richard A.; Skilton, Ryan A.; Parrott, Andrew J. Organic Process Research & Development, Vol. 15, Issue 4 https://doi.org/10.1021/op200109t	journal	May 2011
Automated Multitrajectory Method for Reaction Optimization in a Microfluidic System using Online IR Analysis Moore, Jason S.; Jensen, Klavs F. Organic Process Research & Development, Vol. 16, Issue 8 https://doi.org/10.1021/op300099x	journal	July 2012
An Automated Continuous-Flow Platform for the Estimation of Multistep Reaction Kinetics Reizman, Brandon J.; Jensen, Klavs F. Organic Process Research & Development, Vol. 16, Issue 11 https://doi.org/10.1021/op3001838	journal	October 2012
Breaking the New Bottleneck: Automated Synthesis in Chemical Process Research and Development Harre, Michael; Tilstam, Ulf; Weinmann, Hilmar Organic Process Research & Development, Vol. 3, Issue 5 https://doi.org/10.1021/op990020p	journal	August 1999
Lab automation and robotics: Automation on the move Chapman, Tim Nature, Vol. 421, Issue 6923 https://doi.org/10.1038/421661a	journal	February 2003
1,500 scientists lift the lid on reproducibility Baker, Monya Nature, Vol. 533, Issue 7604 https://doi.org/10.1038/533452a	journal	May 2016
AI in small-molecule drug discovery: a coming wave? Jayatunga, Madura K. P.; Xie, Wen; Ruder, Ludwig Nature Reviews Drug Discovery, Vol. 21, Issue 3 https://doi.org/10.1038/d41573-022-00025-1	journal	February 2022
R&D budgets boom, but success rates falter Mullard, Asher Nature Reviews Drug Discovery, Vol. 21, Issue 4 https://doi.org/10.1038/d41573-022-00051-z	journal	March 2022
The company landscape for artificial intelligence in large-molecule drug discovery Nagra, Navraj S.; van der Veken, Lieven; Stanzl, Erika Nature Reviews Drug Discovery, Vol. 22, Issue 12 https://doi.org/10.1038/d41573-023-00139-0	journal	August 2023
Cloud labs: where robots do the research Arnold, Carrie Nature, Vol. 606, Issue 7914 https://doi.org/10.1038/d41586-022-01618-x	journal	June 2022
AI can help to speed up drug discovery — but only if we give it the right data Mock, Marissa; Edavettal, Suzanne; Langmead, Christopher Nature, Vol. 621, Issue 7979 https://doi.org/10.1038/d41586-023-02896-9	journal	September 2023
AlphaFold touted as next big thing for drug discovery — but is it? Arnold, Carrie Nature, Vol. 622, Issue 7981 https://doi.org/10.1038/d41586-023-02984-w	journal	September 2023
A metal-free organic–inorganic aqueous flow battery Huskinson, Brian; Marshak, Michael P.; Suh, Changwon Nature, Vol. 505, Issue 7482, p. 195-198 https://doi.org/10.1038/nature12909	journal	January 2014
An aqueous, polymer-based redox-flow battery using non-corrosive, safe and low-cost materials Janoschka, Tobias; Martin, Norbert; Martin, Udo Nature, Vol. 527, Issue 7576, p. 78-81 https://doi.org/10.1038/nature15746	journal	October 2015
Machine-learning-assisted materials discovery using failed experiments Raccuglia, Paul; Elbert, Katherine C.; Adler, Philip D. F. Nature, Vol. 533, Issue 7601 https://doi.org/10.1038/nature17439	journal	May 2016
Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking Wang, Mingxun; Carver, Jeremy J.; Phelan, Vanessa V. Nature Biotechnology, Vol. 34, Issue 8 https://doi.org/10.1038/nbt.3597	journal	August 2016
Contemporary screening approaches to reaction discovery and development Collins, Karl D.; Gensch, Tobias; Glorius, Frank Nature Chemistry, Vol. 6, Issue 10 https://doi.org/10.1038/nchem.2062	journal	September 2014
Remote-controlled experiments with cloud chemistry Skilton, Ryan A.; Bourne, Richard A.; Amara, Zacharias Nature Chemistry, Vol. 7, Issue 1 https://doi.org/10.1038/nchem.2143	journal	December 2014
High-quality bulk hybrid perovskite single crystals within minutes by inverse temperature crystallization Saidaminov, Makhsud I.; Abdelhady, Ahmed L.; Murali, Banavoth Nature Communications, Vol. 6, Article No. 7586 https://doi.org/10.1038/ncomms8586	journal	July 2015
Towards sustainable concrete Monteiro, Paulo J. M.; Miller, Sabbie A.; Horvath, Arpad Nature Materials, Vol. 16, Issue 7 https://doi.org/10.1038/nmat4930	journal	June 2017
Autonomy in materials research: a case study in carbon nanotube growth Nikolaev, Pavel; Hooper, Daylond; Webber, Frederick npj Computational Materials, Vol. 2, Issue 1 https://doi.org/10.1038/npjcompumats.2016.31	journal	October 2016
Automating drug discovery Schneider, Gisbert Nature Reviews Drug Discovery, Vol. 17, Issue 2 https://doi.org/10.1038/nrd.2017.232	journal	December 2017
Can open-source R&D reinvigorate drug research? Munos, Bernard Nature Reviews Drug Discovery, Vol. 5, Issue 9 https://doi.org/10.1038/nrd2131	journal	August 2006
Autonomous robotic searching and assembly of two-dimensional crystals to build van der Waals superlattices Masubuchi, Satoru; Morimoto, Masataka; Morikawa, Sei Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-018-03723-w	journal	April 2018
High-throughput discovery of organic cages and catenanes using computational screening fused with robotic synthesis Greenaway, R. L.; Santolini, V.; Bennison, M. J. Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-018-05271-9	journal	July 2018
Networking chemical robots for reaction multitasking Caramelli, Dario; Salley, Daniel; Henson, Alon Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-018-05828-8	journal	August 2018
Capturing chemical intuition in synthesis of metal-organic frameworks Moosavi, Seyed Mohamad; Chidambaram, Arunraj; Talirz, Leopold Nature Communications, Vol. 10, Issue 1 https://doi.org/10.1038/s41467-019-08483-9	journal	February 2019
Building a global alliance of biofoundries Hillson, Nathan; Caddick, Mark; Cai, Yizhi Nature Communications, Vol. 10, Issue 1 https://doi.org/10.1038/s41467-019-10079-2	journal	May 2019
Linking synthesis and structure descriptors from a large collection of synthetic records of zeolite materials Muraoka, Koki; Sada, Yuki; Miyazaki, Daiki Nature Communications, Vol. 10, Issue 1 https://doi.org/10.1038/s41467-019-12394-0	journal	October 2019
Towards a fully automated algorithm driven platform for biosystems design HamediRad, Mohammad; Chao, Ran; Weisberg, Scott Nature Communications, Vol. 10, Issue 1 https://doi.org/10.1038/s41467-019-13189-z	journal	November 2019
Unsupervised discovery of solid-state lithium ion conductors Zhang, Ying; He, Xingfeng; Chen, Zhiqian Nature Communications, Vol. 10, Issue 1 https://doi.org/10.1038/s41467-019-13214-1	journal	November 2019
Autonomous discovery of optically active chiral inorganic perovskite nanocrystals through an intelligent cloud lab Li, Jiagen; Li, Junzi; Liu, Rulin Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-020-15728-5	journal	April 2020
A nanomaterials discovery robot for the Darwinian evolution of shape programmable gold nanoparticles Salley, Daniel; Keenan, Graham; Grizou, Jonathan Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-020-16501-4	journal	June 2020
On-the-fly closed-loop materials discovery via Bayesian active learning Kusne, A. Gilad; Yu, Heshan; Wu, Changming Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-020-19597-w	journal	November 2020
A graph-based network for predicting chemical reaction pathways in solid-state materials synthesis McDermott, Matthew J.; Dwaraknath, Shyam S.; Persson, Kristin A. Nature Communications, Vol. 12, Issue 1 https://doi.org/10.1038/s41467-021-23339-x	journal	May 2021
A self-driving laboratory advances the Pareto front for material properties MacLeod, Benjamin P.; Parlane, Fraser G. L.; Rupnow, Connor C. Nature Communications, Vol. 13, Issue 1 https://doi.org/10.1038/s41467-022-28580-6	journal	February 2022
Autonomous optimization of non-aqueous Li-ion battery electrolytes via robotic experimentation and machine learning coupling Dave, Adarsh; Mitchell, Jared; Burke, Sven Nature Communications, Vol. 13, Issue 1 https://doi.org/10.1038/s41467-022-32938-1	journal	September 2022
AlphaFlow: autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning Volk, Amanda A.; Epps, Robert W.; Yonemoto, Daniel T. Nature Communications, Vol. 14, Issue 1 https://doi.org/10.1038/s41467-023-37139-y	journal	March 2023
Artificial intelligence driven design of catalysts and materials for ring opening polymerization using a domain-specific language Park, Nathaniel H.; Manica, Matteo; Born, Jannis Nature Communications, Vol. 14, Issue 1 https://doi.org/10.1038/s41467-023-39396-3	journal	June 2023
Autonomous and dynamic precursor selection for solid-state materials synthesis Szymanski, Nathan J.; Nevatia, Pragnay; Bartel, Christopher J. Nature Communications, Vol. 14, Issue 1 https://doi.org/10.1038/s41467-023-42329-9	journal	October 2023
An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations Noh, Juran; Doan, Hieu A.; Job, Heather Nature Communications, Vol. 15, Issue 1 https://doi.org/10.1038/s41467-024-47070-5	journal	March 2024
Superlative mechanical energy absorbing efficiency discovered through self-driving lab-human partnership Snapp, Kelsey L.; Verdier, Benjamin; Gongora, Aldair E. Nature Communications, Vol. 15, Issue 1 https://doi.org/10.1038/s41467-024-48534-4	journal	May 2024
Chemically intuited, large-scale screening of MOFs by machine learning techniques Borboudakis, Giorgos; Stergiannakos, Taxiarchis; Frysali, Maria npj Computational Materials, Vol. 3, Issue 1 https://doi.org/10.1038/s41524-017-0045-8	journal	October 2017
Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods Ludwig, Alfred npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0205-0	journal	July 2019
Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains Liang, Qiaohao; Gongora, Aldair E.; Ren, Zekun npj Computational Materials, Vol. 7, Issue 1 https://doi.org/10.1038/s41524-021-00656-9	journal	November 2021
A review of the recent progress in battery informatics Ling, Chen npj Computational Materials, Vol. 8, Issue 1 https://doi.org/10.1038/s41524-022-00713-x	journal	February 2022
A multi-fidelity machine learning approach to high throughput materials screening Fare, Clyde; Fenner, Peter; Benatan, Matthew npj Computational Materials, Vol. 8, Issue 1 https://doi.org/10.1038/s41524-022-00947-9	journal	December 2022
Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification Szymanski, Nathan J.; Bartel, Christopher J.; Zeng, Yan npj Computational Materials, Vol. 9, Issue 1 https://doi.org/10.1038/s41524-023-00984-y	journal	March 2023
Evolution-guided Bayesian optimization for constrained multi-objective optimization in self-driving labs Low, Andre K. Y.; Mekki-Berrada, Flore; Gupta, Abhishek npj Computational Materials, Vol. 10, Issue 1 https://doi.org/10.1038/s41524-024-01274-x	journal	May 2024
Nobel Turing Challenge: creating the engine for scientific discovery Kitano, Hiroaki npj Systems Biology and Applications, Vol. 7, Issue 1 https://doi.org/10.1038/s41540-021-00189-3	journal	June 2021
Data-driven prediction of battery cycle life before capacity degradation Severson, Kristen A.; Attia, Peter M.; Jin, Norman Nature Energy, Vol. 4, Issue 5 https://doi.org/10.1038/s41560-019-0356-8	journal	March 2019
Modular dimerization of organic radicals for stable and dense flow battery catholyte Lv, Xiu-Liang; Sullivan, Patrick T.; Li, Wenjie Nature Energy, Vol. 8, Issue 10 https://doi.org/10.1038/s41560-023-01320-w	journal	August 2023
Flexible automation accelerates materials discovery MacLeod, Benjamin P.; Parlane, Fraser G. L.; Brown, Amanda K. Nature Materials, Vol. 21, Issue 7 https://doi.org/10.1038/s41563-021-01156-3	journal	December 2021
Setting standards to promote progress in bio–nano science Lammers, Twan; Storm, Gert Nature Nanotechnology, Vol. 14, Issue 7 https://doi.org/10.1038/s41565-019-0497-8	journal	July 2019
How to explore chemical space using algorithms and automation Gromski, Piotr S.; Henson, Alon B.; Granda, Jarosław M. Nature Reviews Chemistry, Vol. 3, Issue 2 https://doi.org/10.1038/s41570-018-0066-y	journal	January 2019
Evaluation guidelines for machine learning tools in the chemical sciences Bender, Andreas; Schneider, Nadine; Segler, Marwin Nature Reviews Chemistry, Vol. 6, Issue 6 https://doi.org/10.1038/s41570-022-00391-9	journal	May 2022
From alchemist to AI chemist Greenaway, Rebecca L.; Jelfs, Kim E.; Spivey, Alan C. Nature Reviews Chemistry, Vol. 7, Issue 8 https://doi.org/10.1038/s41570-023-00522-w	journal	July 2023
Organs-on-chips: into the next decade Low, Lucie A.; Mummery, Christine; Berridge, Brian R. Nature Reviews Drug Discovery, Vol. 20, Issue 5 https://doi.org/10.1038/s41573-020-0079-3	journal	September 2020
Engineering precision nanoparticles for drug delivery Mitchell, Michael J.; Billingsley, Margaret M.; Haley, Rebecca M. Nature Reviews Drug Discovery, Vol. 20, Issue 2 https://doi.org/10.1038/s41573-020-0090-8	journal	December 2020
Emerging applications of zeolites in catalysis, separation and host–guest assembly Li, Yi; Yu, Jihong Nature Reviews Materials, Vol. 6, Issue 12 https://doi.org/10.1038/s41578-021-00347-3	journal	August 2021
Machine learning for a sustainable energy future Yao, Zhenpeng; Lum, Yanwei; Johnston, Andrew Nature Reviews Materials, Vol. 8, Issue 3 https://doi.org/10.1038/s41578-022-00490-5	journal	October 2022
Controlling an organic synthesis robot with machine learning to search for new reactivity Granda, Jarosław M.; Donina, Liva; Dragone, Vincenza Nature, Vol. 559, Issue 7714 https://doi.org/10.1038/s41586-018-0307-8	journal	July 2018
Machine learning for molecular and materials science Butler, Keith T.; Davies, Daniel W.; Cartwright, Hugh Nature, Vol. 559, Issue 7715 https://doi.org/10.1038/s41586-018-0337-2	journal	July 2018
An open-source drug discovery platform enables ultra-large virtual screens Gorgulla, Christoph; Boeszoermenyi, Andras; Wang, Zi-Fu Nature, Vol. 580, Issue 7805 https://doi.org/10.1038/s41586-020-2117-z	journal	March 2020
A mobile robotic chemist Burger, Benjamin; Maffettone, Phillip M.; Gusev, Vladimir V. Nature, Vol. 583, Issue 7815 https://doi.org/10.1038/s41586-020-2442-2	journal	July 2020
Bayesian reaction optimization as a tool for chemical synthesis Shields, Benjamin J.; Stevens, Jason; Li, Jun Nature, Vol. 590, Issue 7844 https://doi.org/10.1038/s41586-021-03213-y	journal	February 2021
Highly accurate protein structure prediction with AlphaFold Jumper, John; Evans, Richard; Pritzel, Alexander Nature https://doi.org/10.1038/s41586-021-03819-2	journal	July 2021
Synthon-based ligand discovery in virtual libraries of over 11 billion compounds Sadybekov, Arman A.; Sadybekov, Anastasiia V.; Liu, Yongfeng Nature, Vol. 601, Issue 7893 https://doi.org/10.1038/s41586-021-04220-9	journal	December 2021
FAIR data enabling new horizons for materials research Scheffler, Matthias; Aeschlimann, Martin; Albrecht, Martin Nature, Vol. 604, Issue 7907 https://doi.org/10.1038/s41586-022-04501-x	journal	April 2022
Computational approaches streamlining drug discovery Sadybekov, Anastasiia V.; Katritch, Vsevolod Nature, Vol. 616, Issue 7958 https://doi.org/10.1038/s41586-023-05905-z	journal	April 2023
Scientific discovery in the age of artificial intelligence Wang, Hanchen; Fu, Tianfan; Du, Yuanqi Nature, Vol. 620, Issue 7972 https://doi.org/10.1038/s41586-023-06221-2	journal	August 2023
De novo design of protein structure and function with RFdiffusion Watson, Joseph L.; Juergens, David; Bennett, Nathaniel R. Nature, Vol. 620, Issue 7976 https://doi.org/10.1038/s41586-023-06415-8	journal	July 2023
Illuminating protein space with a programmable generative model Ingraham, John B.; Baranov, Max; Costello, Zak Nature, Vol. 623, Issue 7989 https://doi.org/10.1038/s41586-023-06728-8	journal	November 2023
Scaling deep learning for materials discovery Merchant, Amil; Batzner, Simon; Schoenholz, Samuel S. Nature, Vol. 624, Issue 7990 https://doi.org/10.1038/s41586-023-06735-9	journal	November 2023
Autonomous chemical research with large language models Boiko, Daniil A.; MacKnight, Robert; Kline, Ben Nature, Vol. 624, Issue 7992 https://doi.org/10.1038/s41586-023-06792-0	journal	December 2023
Identifying general reaction conditions by bandit optimization Wang, Jason Y.; Stevens, Jason M.; Kariofillis, Stavros K. Nature, Vol. 626, Issue 8001 https://doi.org/10.1038/s41586-024-07021-y	journal	February 2024
Artificial intelligence and illusions of understanding in scientific research Messeri, Lisa; Crockett, M. J. Nature, Vol. 627, Issue 8002 https://doi.org/10.1038/s41586-024-07146-0	journal	March 2024
Deep learning enables rapid identification of potent DDR1 kinase inhibitors Zhavoronkov, Alex; Ivanenkov, Yan A.; Aliper, Alex Nature Biotechnology, Vol. 37, Issue 9 https://doi.org/10.1038/s41587-019-0224-x	journal	September 2019
Catalysis-Hub.org, an open electronic structure database for surface reactions Winther, Kirsten T.; Hoffmann, Max J.; Boes, Jacob R. Scientific Data, Vol. 6, Issue 1 https://doi.org/10.1038/s41597-019-0081-y	journal	May 2019
Text-mined dataset of inorganic materials synthesis recipes Kononova, Olga; Huo, Haoyan; He, Tanjin Scientific Data, Vol. 6, Issue 1 https://doi.org/10.1038/s41597-019-0224-1	journal	October 2019
High-throughput screening platform for solid electrolytes combining hierarchical ion-transport prediction algorithms He, Bing; Chi, Shuting; Ye, Anjiang Scientific Data, Vol. 7, Issue 1 https://doi.org/10.1038/s41597-020-0474-y	journal	May 2020
SOMAS: a platform for data-driven material discovery in redox flow battery development Gao, Peiyuan; Andersen, Amity; Sepulveda, Jonathan Scientific Data, Vol. 9, Issue 1 https://doi.org/10.1038/s41597-022-01814-4	journal	December 2022
Optimization of Molecules via Deep Reinforcement Learning Zhou, Zhenpeng; Kearnes, Steven; Li, Li Scientific Reports, Vol. 9, Issue 1 https://doi.org/10.1038/s41598-019-47148-x	journal	July 2019
A Kriging-Based Approach to Autonomous Experimentation with Applications to X-Ray Scattering Noack, Marcus M.; Yager, Kevin G.; Fukuto, Masafumi Scientific Reports, Vol. 9, Issue 1 https://doi.org/10.1038/s41598-019-48114-3	journal	August 2019
Efficient Closed-loop Maximization of Carbon Nanotube Growth Rate using Bayesian Optimization Chang, Jorge; Nikolaev, Pavel; Carpena-Núñez, Jennifer Scientific Reports, Vol. 10, Issue 1 https://doi.org/10.1038/s41598-020-64397-3	journal	June 2020
Principles of computer-controlled linear motion applied to an open-source affordable liquid handler for automated micropipetting Florian, David C.; Odziomek, Mateusz; Ock, Cerie L. Scientific Reports, Vol. 10, Issue 1 https://doi.org/10.1038/s41598-020-70465-5	journal	August 2020
Autonomous materials discovery driven by Gaussian process regression with inhomogeneous measurement noise and anisotropic kernels Noack, Marcus M.; Doerk, Gregory S.; Li, Ruipeng Scientific Reports, Vol. 10, Issue 1 https://doi.org/10.1038/s41598-020-74394-1	journal	October 2020
Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution Tran, Kevin; Ulissi, Zachary W. Nature Catalysis, Vol. 1, Issue 9 https://doi.org/10.1038/s41929-018-0142-1	journal	September 2018
Rapid virtual screening of enantioselective catalysts using CatVS Rosales, Anthony R.; Wahlers, Jessica; Limé, Elaine Nature Catalysis, Vol. 2, Issue 1 https://doi.org/10.1038/s41929-018-0193-3	journal	December 2018
Solar methanol energy storage Tountas, Athanasios A.; Ozin, Geoffrey A.; Sain, Mohini M. Nature Catalysis, Vol. 4, Issue 11 https://doi.org/10.1038/s41929-021-00696-w	journal	November 2021
Interpretable machine learning for knowledge generation in heterogeneous catalysis Esterhuizen, Jacques A.; Goldsmith, Bryan R.; Linic, Suljo Nature Catalysis, Vol. 5, Issue 3 https://doi.org/10.1038/s41929-022-00744-z	journal	March 2022
Bridging the complexity gap in computational heterogeneous catalysis with machine learning Mou, Tianyou; Pillai, Hemanth Somarajan; Wang, Siwen Nature Catalysis, Vol. 6, Issue 2 https://doi.org/10.1038/s41929-023-00911-w	journal	February 2023
An automated Design-Build-Test-Learn pipeline for enhanced microbial production of fine chemicals Carbonell, Pablo; Jervis, Adrian J.; Robinson, Christopher J. Communications Biology, Vol. 1, Issue 1 https://doi.org/10.1038/s42003-018-0076-9	journal	June 2018
Data-science driven autonomous process optimization Christensen, Melodie; Yunker, Lars P. E.; Adedeji, Folarin Communications Chemistry, Vol. 4, Issue 1 https://doi.org/10.1038/s42004-021-00550-x	journal	August 2021
On scientific understanding with artificial intelligence Krenn, Mario; Pollice, Robert; Guo, Si Yue Nature Reviews Physics, Vol. 4, Issue 12 https://doi.org/10.1038/s42254-022-00518-3	journal	October 2022
Experimental discovery of structure–property relationships in ferroelectric materials via active learning Liu, Yongtao; Kelley, Kyle P.; Vasudevan, Rama K. Nature Machine Intelligence, Vol. 4, Issue 4 https://doi.org/10.1038/s42256-022-00460-0	journal	April 2022
Dual use of artificial-intelligence-powered drug discovery Urbina, Fabio; Lentzos, Filippa; Invernizzi, Cédric Nature Machine Intelligence, Vol. 4, Issue 3 https://doi.org/10.1038/s42256-022-00465-9	journal	March 2022
Multi-objective goal-directed optimization of de novo stable organic radicals for aqueous redox flow batteries S. V., Shree Sowndarya; Law, Jeffrey N.; Tripp, Charles E. Nature Machine Intelligence, Vol. 4, Issue 8 https://doi.org/10.1038/s42256-022-00506-3	journal	August 2022
Reinforcement learning supercharges redox flow batteries Cao, Yang; Ser, Cher Tian; Skreta, Marta Nature Machine Intelligence, Vol. 4, Issue 8 https://doi.org/10.1038/s42256-022-00523-2	journal	August 2022
Large-scale chemical language representations capture molecular structure and properties Ross, Jerret; Belgodere, Brian; Chenthamarakshan, Vijil Nature Machine Intelligence, Vol. 4, Issue 12 https://doi.org/10.1038/s42256-022-00580-7	journal	December 2022
Biological research and self-driving labs in deep space supported by artificial intelligence Sanders, Lauren M.; Scott, Ryan T.; Yang, Jason H. Nature Machine Intelligence, Vol. 5, Issue 3 https://doi.org/10.1038/s42256-023-00618-4	journal	March 2023
Augmenting large language models with chemistry tools M. Bran, Andres; Cox, Sam; Schilter, Oliver Nature Machine Intelligence, Vol. 6, Issue 5 https://doi.org/10.1038/s42256-024-00832-8	journal	May 2024
Best practices in lithium battery cell preparation and evaluation Dai, Fang; Cai, Mei Communications Materials, Vol. 3, Issue 1 https://doi.org/10.1038/s43246-022-00286-8	journal	September 2022
Bayesian optimization as a valuable tool for sustainable chemical reaction development Braconi, Elena Nature Reviews Methods Primers, Vol. 3, Issue 1 https://doi.org/10.1038/s43586-023-00266-3	journal	October 2023
The rise of self-driving labs in chemical and materials sciences Abolhasani, Milad; Kumacheva, Eugenia Nature Synthesis, Vol. 2, Issue 6 https://doi.org/10.1038/s44160-022-00231-0	journal	January 2023
A robotic platform for the synthesis of colloidal nanocrystals Zhao, Haitao; Chen, Wei; Huang, Hao Nature Synthesis, Vol. 2, Issue 6 https://doi.org/10.1038/s44160-023-00250-5	journal	March 2023
Genetic Algorithms Holland, John H. Scientific American, Vol. 267, Issue 1 https://doi.org/10.1038/scientificamerican0792-66	journal	July 1992
The FAIR Guiding Principles for scientific data management and stewardship Wilkinson, Mark D.; Dumontier, Michel; Aalbersberg, IJsbrand Jan Scientific Data, Vol. 3, Issue 1 https://doi.org/10.1038/sdata.2016.18	journal	March 2016
Origin and evolution of high throughput screening Pereira, D. A.; Williams, J. A. British Journal of Pharmacology, Vol. 152, Issue 1 https://doi.org/10.1038/sj.bjp.0707373	journal	September 2007
Computational design of molecules for an all-quinone redox flow battery Er, Süleyman; Suh, Changwon; Marshak, Michael P. Chemical Science, Vol. 6, Issue 2, p. 885-893 https://doi.org/10.1039/C4SC03030C	journal	January 2015
A self optimizing synthetic organic reactor system using real-time in-line NMR spectroscopy Sans, Victor; Porwol, Luzian; Dragone, Vincenza Chemical Science, Vol. 6, Issue 2 https://doi.org/10.1039/C4SC03075C	journal	January 2015
Simultaneous solvent screening and reaction optimization in microliter slugs Reizman, Brandon J.; Jensen, Klavs F. Chemical Communications, Vol. 51, Issue 68 https://doi.org/10.1039/C5CC03651H	journal	January 2015
Towards dial-a-molecule by integrating continuous flow, analytics and self-optimisation Sans, Victor; Cronin, Leroy Chemical Society Reviews, Vol. 45, Issue 8 https://doi.org/10.1039/C5CS00793C	journal	January 2016
Online quantitative mass spectrometry for the rapid adaptive optimisation of automated flow reactors Holmes, Nicholas; Akien, Geoffrey R.; Savage, Robert J. D. Reaction Chemistry & Engineering, Vol. 1, Issue 1 https://doi.org/10.1039/C5RE00083A	journal	January 2016
Multi-step continuous-flow synthesis Britton, Joshua; Raston, Colin L. Chemical Society Reviews, Vol. 46, Issue 5 https://doi.org/10.1039/C6CS00830E	journal	January 2017
Self-optimisation of the final stage in the synthesis of EGFR kinase inhibitor AZD9291 using an automated flow reactor Holmes, Nicholas; Akien, Geoffrey R.; Blacker, A. John Reaction Chemistry & Engineering, Vol. 1, Issue 4 https://doi.org/10.1039/C6RE00059B	journal	January 2016
Suzuki–Miyaura cross-coupling optimization enabled by automated feedback Reizman, Brandon J.; Wang, Yi-Ming; Buchwald, Stephen L. Reaction Chemistry & Engineering, Vol. 1, Issue 6 https://doi.org/10.1039/C6RE00153J	journal	January 2016
A new class of chiral semiconductors: chiral-organic-molecule-incorporating organic–inorganic hybrid perovskites Ahn, Jihoon; Lee, Eunsong; Tan, Jeiwan Materials Horizons, Vol. 4, Issue 5 https://doi.org/10.1039/C7MH00197E	journal	January 2017
Efficient prediction of reaction paths through molecular graph and reaction network analysis Kim, Yeonjoon; Kim, Jin Woo; Kim, Zeehyo Chemical Science, Vol. 9, Issue 4 https://doi.org/10.1039/C7SC03628K	journal	January 2018
Energy-based descriptors to rapidly predict hydrogen storage in metal–organic frameworks Bucior, Benjamin J.; Bobbitt, N. Scott; Islamoglu, Timur Molecular Systems Design & Engineering, Vol. 4, Issue 1 https://doi.org/10.1039/C8ME00050F	journal	January 2019
Optimum catalyst selection over continuous and discrete process variables with a single droplet microfluidic reaction platform Baumgartner, Lorenz M.; Coley, Connor W.; Reizman, Brandon J. Reaction Chemistry & Engineering, Vol. 3, Issue 3 https://doi.org/10.1039/C8RE00032H	journal	January 2018
OpenFlowChem – a platform for quick, robust and flexible automation and self-optimisation of flow chemistry Cherkasov, Nikolay; Bai, Yang; Expósito, Antonio José Reaction Chemistry & Engineering, Vol. 3, Issue 5 https://doi.org/10.1039/C8RE00046H	journal	January 2018
Chimera: enabling hierarchy based multi-objective optimization for self-driving laboratories Häse, Florian; Roch, Loïc M.; Aspuru-Guzik, Alán Chemical Science, Vol. 9, Issue 39 https://doi.org/10.1039/C8SC02239A	journal	January 2018
Exploration of flow reaction conditions using machine-learning for enantioselective organocatalyzed Rauhut–Currier and [3+2] annulation sequence Kondo, Masaru; Wathsala, H. D. P.; Sako, Makoto Chemical Communications, Vol. 56, Issue 8 https://doi.org/10.1039/C9CC08526B	journal	January 2020
An autonomous self-optimizing flow machine for the synthesis of pyridine–oxazoline (PyOX) ligands Wimmer, Eric; Cortés-Borda, Daniel; Brochard, Solène Reaction Chemistry & Engineering, Vol. 4, Issue 9 https://doi.org/10.1039/C9RE00096H	journal	January 2019
Automated platforms for reaction self-optimization in flow Mateos, Carlos; Nieves-Remacha, María José; Rincón, Juan A. Reaction Chemistry & Engineering, Vol. 4, Issue 9 https://doi.org/10.1039/C9RE00116F	journal	January 2019
Algorithms for the self-optimisation of chemical reactions Clayton, Adam D.; Manson, Jamie A.; Taylor, Connor J. Reaction Chemistry & Engineering, Vol. 4, Issue 9 https://doi.org/10.1039/C9RE00209J	journal	January 2019
Mining predicted crystal structure landscapes with high throughput crystallisation: old molecules, new insights Cui, Peng; McMahon, David P.; Spackman, Peter R. Chemical Science, Vol. 10, Issue 43 https://doi.org/10.1039/C9SC02832C	journal	January 2019
Benchmarking the acceleration of materials discovery by sequential learning Rohr, Brian; Stein, Helge S.; Guevarra, Dan Chemical Science, Vol. 11, Issue 10 https://doi.org/10.1039/C9SC05999G	journal	January 2020
Machine-learning-assisted high-throughput computational screening of high performance metal–organic frameworks Shi, Zenan; Yang, Wenyuan; Deng, Xiaomei Molecular Systems Design & Engineering, Vol. 5, Issue 4 https://doi.org/10.1039/D0ME00005A	journal	January 2020
Automated microfluidic screening of ligand interactions during the synthesis of cesium lead bromide nanocrystals Li, Shangkun; Baker, Robert W.; Lignos, Ioannis Molecular Systems Design & Engineering, Vol. 5, Issue 6 https://doi.org/10.1039/D0ME00008F	journal	January 2020
Iterative experimental design based on active machine learning reduces the experimental burden associated with reaction screening Eyke, Natalie S.; Green, William H.; Jensen, Klavs F. Reaction Chemistry & Engineering, Vol. 5, Issue 10 https://doi.org/10.1039/D0RE00232A	journal	January 2020
Accelerated AI development for autonomous materials synthesis in flow Epps, Robert W.; Volk, Amanda A.; Reyes, Kristofer G. Chemical Science, Vol. 12, Issue 17 https://doi.org/10.1039/D0SC06463G	journal	January 2021
Accelerating high-throughput virtual screening through molecular pool-based active learning Graff, David E.; Shakhnovich, Eugene I.; Coley, Connor W. Chemical Science, Vol. 12, Issue 22 https://doi.org/10.1039/D0SC06805E	journal	January 2021
Autonomous optimisation of a nanoparticle catalysed reduction reaction in continuous flow Hall, Brendan L.; Taylor, Connor J.; Labes, Ricardo Chemical Communications, Vol. 57, Issue 40 https://doi.org/10.1039/D1CC00859E	journal	January 2021
Toward autonomous design and synthesis of novel inorganic materials Szymanski, Nathan J.; Zeng, Yan; Huo, Haoyan Materials Horizons, Vol. 8, Issue 8 https://doi.org/10.1039/D1MH00495F	journal	January 2021
Predictive high-throughput screening of PEGylated lipids in oligonucleotide-loaded lipid nanoparticles for neuronal gene silencing Sarode, Apoorva; Fan, Yuchen; Byrnes, Amy E. Nanoscale Advances, Vol. 4, Issue 9 https://doi.org/10.1039/D1NA00712B	journal	January 2022
Accelerated screening of colloidal nanocrystals using artificial neural network-assisted autonomous flow reactor technology Vikram, Ajit; Brudnak, Ken; Zahid, Arwa Nanoscale, Vol. 13, Issue 40 https://doi.org/10.1039/D1NR05497J	journal	January 2021
Automated multi-objective reaction optimisation: which algorithm should I use? Müller, Pia; Clayton, Adam D.; Manson, Jamie Reaction Chemistry & Engineering, Vol. 7, Issue 4 https://doi.org/10.1039/D1RE00549A	journal	January 2022
Golem: an algorithm for robust experiment and process optimization Aldeghi, Matteo; Häse, Florian; Hickman, Riley J. Chemical Science, Vol. 12, Issue 44 https://doi.org/10.1039/D1SC01545A	journal	January 2021
Automation isn't automatic Christensen, Melodie; Yunker, Lars P. E.; Shiri, Parisa Chemical Science, Vol. 12, Issue 47 https://doi.org/10.1039/D1SC04588A	journal	January 2021
Bayesian optimization with constraint on passed charge for multiparameter screening of electrochemical reductive carboxylation in a flow microreactor Naito, Yuki; Kondo, Masaru; Nakamura, Yuto Chemical Communications, Vol. 58, Issue 24 https://doi.org/10.1039/D2CC00124A	journal	January 2022
Parallel tempered genetic algorithm guided by deep neural networks for inverse molecular design Nigam, AkshatKumar; Pollice, Robert; Aspuru-Guzik, Alán Digital Discovery, Vol. 1, Issue 4 https://doi.org/10.1039/D2DD00003B	journal	January 2022
A self-driving laboratory designed to accelerate the discovery of adhesive materials Rooney, Michael B.; MacLeod, Benjamin P.; Oldford, Ryan Digital Discovery, Vol. 1, Issue 4 https://doi.org/10.1039/D2DD00029F	journal	January 2022
Robotic cell assembly to accelerate battery research Zhang, Bojing; Merker, Leon; Sanin, Alexey Digital Discovery, Vol. 1, Issue 6, p. 755-762 https://doi.org/10.1039/D2DD00046F	journal	January 2022
Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS Tom, Gary; Hickman, Riley J.; Zinzuwadia, Aniket Digital Discovery, Vol. 2, Issue 3 https://doi.org/10.1039/D2DD00146B	journal	January 2023
High-throughput and data driven strategies for the design of deep-eutectic solvent electrolytes Rodriguez, Jaime; Politi, Maria; Adler, Stuart Molecular Systems Design & Engineering, Vol. 7, Issue 8 https://doi.org/10.1039/D2ME00050D	journal	January 2022
Autonomous polymer synthesis delivered by multi-objective closed-loop optimisation Knox, Stephen T.; Parkinson, Sam J.; Wilding, Clarissa Y. P. Polymer Chemistry, Vol. 13, Issue 11 https://doi.org/10.1039/D2PY00040G	journal	January 2022
AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor Ren, Feng; Ding, Xiao; Zheng, Min Chemical Science, Vol. 14, Issue 6 https://doi.org/10.1039/D2SC05709C	journal	January 2023
A high-throughput workflow for the synthesis of CdSe nanocrystals using a sonochemical materials acceleration platform Politi, Maria; Baum, Fabio; Vaddi, Kiran Digital Discovery, Vol. 2, Issue 4 https://doi.org/10.1039/D3DD00033H	journal	January 2023
Autonomous biomimetic solid dispensing using a dual-arm robotic manipulator Jiang, Ying; Fakhruldeen, Hatem; Pizzuto, Gabriella Digital Discovery, Vol. 2, Issue 6, p. 1733-1744 https://doi.org/10.1039/D3DD00075C	journal	January 2023
Go with the flow: deep learning methods for autonomous viscosity estimations Walker, Michael; Pizzuto, Gabriella; Fakhruldeen, Hatem Digital Discovery, Vol. 2, Issue 5, p. 1540-1547 https://doi.org/10.1039/D3DD00109A	journal	September 2023
14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon Jablonka, Kevin Maik; Ai, Qianxiang; Al-Feghali, Alexander Digital Discovery, Vol. 2, Issue 5 https://doi.org/10.1039/D3DD00113J	journal	January 2023
Digital pipette: open hardware for liquid transfer in self-driving laboratories Yoshikawa, Naruki; Darvish, Kourosh; Vakili, Mohammad Ghazi Digital Discovery, Vol. 2, Issue 6 https://doi.org/10.1039/D3DD00115F	journal	January 2023
Orchestrating nimble experiments across interconnected labs Guevarra, Dan; Kan, Kevin; Lai, Yungchieh Digital Discovery, Vol. 2, Issue 6 https://doi.org/10.1039/D3DD00166K	journal	January 2023
Review of low-cost self-driving laboratories in chemistry and materials science: the “frugal twin” concept Lo, Stanley; Baird, Sterling G.; Schrier, Joshua Digital Discovery, Vol. 3, Issue 5, p. 842-868 https://doi.org/10.1039/D3DD00223C	journal	January 2024
A field guide to flow chemistry for synthetic organic chemists Capaldo, Luca; Wen, Zhenghui; Noël, Timothy Chemical Science, Vol. 14, Issue 16, p. 4230-4247 https://doi.org/10.1039/D3SC00992K	journal	March 2023
Machine learning-guided high throughput nanoparticle design Ortiz-Perez, Ana; van Tilborg, Derek; van der Meel, Roy Digital Discovery, Vol. 3, Issue 7 https://doi.org/10.1039/D4DD00104D	journal	January 2024
Automated multiparameter optimisation of high-performance liquid chromatographic separations using the sequential simplex procedure Berridge, John C. The Analyst, Vol. 109, Issue 3 https://doi.org/10.1039/an9840900291	journal	January 1984
Annual Chemical Congress James, E. B.; Simons, J. V.; Bateman, D. C. Analytical Proceedings, Vol. 19, Issue 10 https://doi.org/10.1039/ap9821900462	journal	January 1982
Short papers in pharmaceutical analysis Wright, A. G.; Fell, A. F.; Berridge, J. C. Analytical Proceedings, Vol. 25, Issue 9 https://doi.org/10.1039/ap9882500300	journal	January 1988
On-line analysis of CdSe nanoparticle formation in a continuous flow chip-based microreactor Krishnadasan, S.; Tovilla, J.; Vilar, R. Journal of Materials Chemistry, Vol. 14, Issue 17 https://doi.org/10.1039/b401559b	journal	January 2004
Macro-to-micro interfaces for microfluidic devices Fredrickson, Carl K.; Fan, Z. Hugh Lab on a Chip, Vol. 4, Issue 6 https://doi.org/10.1039/b410720a	journal	January 2004
Intelligent routes to the controlled synthesis of nanoparticles Krishnadasan, S.; Brown, R. J. C.; deMello, A. J. Lab on a Chip, Vol. 7, Issue 11 https://doi.org/10.1039/b711412e	journal	January 2007
Robotic work station for microscale synthetic chemistry: On-line absorption spectroscopy, quantitative automated thin-layer chromatography, and multiple reactions in parallel Lindsey, Jonathan S.; Corkan, L. Andrew; Erb, David Review of Scientific Instruments, Vol. 59, Issue 6 https://doi.org/10.1063/1.1139755	journal	June 1988
Measurement and Control of Magnetic Field Homogeneity Ernst, Richard R. Review of Scientific Instruments, Vol. 39, Issue 7 https://doi.org/10.1063/1.1683586	journal	July 1968
Applications of high throughput (combinatorial) methodologies to electronic, magnetic, optical, and energy-related materials Green, Martin L.; Takeuchi, Ichiro; Hattrick-Simpers, Jason R. Journal of Applied Physics, Vol. 113, Issue 23 https://doi.org/10.1063/1.4803530	journal	June 2013
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation Jain, Anubhav; Ong, Shyue Ping; Hautier, Geoffroy APL Materials, Vol. 1, Issue 1 https://doi.org/10.1063/1.4812323	journal	July 2013
Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloys Tran, Anh; Tranchida, Julien; Wildey, Tim The Journal of Chemical Physics, Vol. 153, Issue 7 https://doi.org/10.1063/5.0015672	journal	August 2020
Autonomous materials synthesis by machine learning and robotics Shimizu, Ryota; Kobayashi, Shigeru; Watanabe, Yuki APL Materials, Vol. 8, Issue 11 https://doi.org/10.1063/5.0020370	journal	November 2020
G ryffin : An algorithm for Bayesian optimization of categorical variables informed by expert knowledge Häse, Florian; Aldeghi, Matteo; Hickman, Riley J. Applied Physics Reviews, Vol. 8, Issue 3 https://doi.org/10.1063/5.0048164	journal	September 2021
Genomic responses in mouse models poorly mimic human inflammatory diseases Seok, Junhee; Warren, H. Shaw; Cuenca, Alex G. Proceedings of the National Academy of Sciences, Vol. 110, Issue 9 https://doi.org/10.1073/pnas.1222878110	journal	February 2013
Data-driven electrolyte design for lithium metal anodes Kim, Sang Cheol; Oyakhire, Solomon T.; Athanitis, Constantine Proceedings of the National Academy of Sciences, Vol. 120, Issue 10 https://doi.org/10.1073/pnas.2214357120	journal	February 2023
Applications of Optimization Strategies in the Design of Intelligent Laboratory Robotic Procedures Lochmüller, C. H.; Lung, K. R.; Cousins, K. R. Analytical Letters, Vol. 18, Issue 4 https://doi.org/10.1080/00032718508066145	journal	January 1985
Discrimination Among Mechanistic Models Box, G. E. P.; Hill, W. J. Technometrics, Vol. 9, Issue 1 https://doi.org/10.1080/00401706.1967.10490441	journal	February 1967
Combining Gaussian processes, mutual information and a genetic algorithm for multi-target optimization of expensive-to-evaluate functions Peremezhney, N.; Hines, E.; Lapkin, A. Engineering Optimization, Vol. 46, Issue 11 https://doi.org/10.1080/0305215X.2014.881997	journal	November 2013
A Review of Simplex Optimization in Analytical Chemistry Deming, Stanley N.; Parker, Lloyd R.; Bonner Denton, M. C R C Critical Reviews in Analytical Chemistry, Vol. 7, Issue 3 https://doi.org/10.1080/10408347808542701	journal	January 1978
Closed-loop discovery platform integration is needed for artificial intelligence to make an impact in drug discovery Saikin, Semion K.; Kreisbeck, Christoph; Sheberla, Dennis Expert Opinion on Drug Discovery, Vol. 14, Issue 1 https://doi.org/10.1080/17460441.2019.1546690	journal	November 2018
NIMS-OS: an automation software to implement a closed loop between artificial intelligence and robotic experiments in materials science Tamura, Ryo; Tsuda, Koji; Matsuda, Shoichi Science and Technology of Advanced Materials: Methods, Vol. 3, Issue 1 https://doi.org/10.1080/27660400.2023.2232297	journal	July 2023
Prompt engineering of GPT-4 for chemical research: what can/cannot be done? Hatakeyama-Sato, Kan; Yamane, Naoki; Igarashi, Yasuhiko Science and Technology of Advanced Materials: Methods, Vol. 3, Issue 1 https://doi.org/10.1080/27660400.2023.2260300	journal	October 2023
Combinatorial investigations of advanced Li-ion rechargeable battery electrode materials Fleischauer, M. D.; Hatchard, T. D.; Bonakdarpour, A. Measurement Science and Technology, Vol. 16, Issue 1 https://doi.org/10.1088/0957-0233/16/1/028	journal	December 2004
Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation Krenn, Mario; Häse, Florian; Nigam, AkshatKumar Machine Learning: Science and Technology, Vol. 1, Issue 4 https://doi.org/10.1088/2632-2153/aba947	journal	November 2020
Olympus: a benchmarking framework for noisy optimization and experiment planning Häse, Florian; Aldeghi, Matteo; Hickman, Riley J. Machine Learning: Science and Technology, Vol. 2, Issue 3 https://doi.org/10.1088/2632-2153/abedc8	journal	July 2021
Physics makes the difference: Bayesian optimization and active learning via augmented Gaussian process Ziatdinov, Maxim A.; Ghosh, Ayana; Kalinin, Sergei V. Machine Learning: Science and Technology, Vol. 3, Issue 1 https://doi.org/10.1088/2632-2153/ac4baa	journal	February 2022
Some aspects of the sequential design of experiments Robbins, Herbert Bulletin of the American Mathematical Society, Vol. 58, Issue 5 https://doi.org/10.1090/S0002-9904-1952-09620-8	journal	January 1952
Comparative analysis of molecular fingerprints in prediction of drug combination effects Zagidullin, B.; Wang, Z.; Guan, Y. Briefings in Bioinformatics, Vol. 22, Issue 6 https://doi.org/10.1093/bib/bbab291	journal	August 2021
openBIS ELN-LIMS: an open-source database for academic laboratories Barillari, Caterina; Ottoz, Diana S. M.; Fuentes-Serna, Juan Mariano Bioinformatics, Vol. 32, Issue 4 https://doi.org/10.1093/bioinformatics/btv606	journal	October 2015
Use of the Simplex Method to Optimize Analytical Conditions in Clinical Chemistry Krause, Richard D.; Lott, John A. Clinical Chemistry, Vol. 20, Issue 7 https://doi.org/10.1093/clinchem/20.7.775	journal	July 1974
Evaluation of Trinder's Glucose Oxidase Method for Measuring Glucose in Serum and Urine Lott, John A.; Turner, Kathie Clinical Chemistry, Vol. 21, Issue 12 https://doi.org/10.1093/clinchem/21.12.1754	journal	November 1975
Automated development of a kinetic method for the continuous-flow determination of creatinine. Olansky, A. S.; Deming, S. N. Clinical Chemistry, Vol. 24, Issue 12 https://doi.org/10.1093/clinchem/24.12.2115	journal	December 1978
A Simplex Method for Function Minimization Nelder, J. A.; Mead, R. The Computer Journal, Vol. 7, Issue 4 https://doi.org/10.1093/comjnl/7.4.308	journal	January 1965
The Protein Data Bank Berman, H. M. Nucleic Acids Research, Vol. 28, Issue 1 https://doi.org/10.1093/nar/28.1.235	journal	January 2000
PubChem 2023 update Kim, Sunghwan; Chen, Jie; Cheng, Tiejun Nucleic Acids Research, Vol. 51, Issue D1 https://doi.org/10.1093/nar/gkac956	journal	October 2022
Crystallography Open Database (COD): an open-access collection of crystal structures and platform for world-wide collaboration Gražulis, Saulius; Daškevič, Adriana; Merkys, Andrius Nucleic Acids Research, Vol. 40, Issue D1 https://doi.org/10.1093/nar/gkr900	journal	November 2011
An all-round AI-Chemist with a scientific mind Zhu, Qing; Zhang, Fei; Huang, Yan National Science Review, Vol. 9, Issue 10 https://doi.org/10.1093/nsr/nwac190	journal	September 2022
Cheaper faster drug development validated by the repositioning of drugs against neglected tropical diseases Williams, Kevin; Bilsland, Elizabeth; Sparkes, Andrew Journal of The Royal Society Interface, Vol. 12, Issue 104 https://doi.org/10.1098/rsif.2014.1289	journal	March 2015
Self-driving laboratories to autonomously navigate the protein fitness landscape Rapp, Jacob T.; Bremer, Bennett J.; Romero, Philip A. bioRxiv https://doi.org/10.1101/2023.05.20.541582	preprint	May 2023
Challenges in High-Throughput Inorganic Materials Prediction and Autonomous Synthesis Leeman, Josh; Liu, Yuhan; Stiles, Joseph PRX Energy, Vol. 3, Issue 1 https://doi.org/10.1103/PRXEnergy.3.011002	journal	March 2024
SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates Ouyang, Runhai; Curtarolo, Stefano; Ahmetcik, Emre Physical Review Materials, Vol. 2, Issue 8 https://doi.org/10.1103/PhysRevMaterials.2.083802	journal	August 2018
Recent developments in the Inorganic Crystal Structure Database: theoretical crystal structure data and related features Zagorac, D.; Müller, H.; Ruehl, S. Journal of Applied Crystallography, Vol. 52, Issue 5 https://doi.org/10.1107/S160057671900997X	journal	September 2019
No free lunch theorems for optimization Wolpert, D. H.; Macready, W. G. IEEE Transactions on Evolutionary Computation, Vol. 1, Issue 1 https://doi.org/10.1109/4235.585893	journal	April 1997
The particle swarm - explosion, stability, and convergence in a multidimensional complex space Clerc, M.; Kennedy, J. IEEE Transactions on Evolutionary Computation, Vol. 6, Issue 1 https://doi.org/10.1109/4235.985692	journal	January 2002
A fast and elitist multiobjective genetic algorithm: NSGA-II Deb, K.; Pratap, A.; Agarwal, S. IEEE Transactions on Evolutionary Computation, Vol. 6, Issue 2 https://doi.org/10.1109/4235.996017	journal	April 2002
Pymoo: Multi-Objective Optimization in Python Blank, Julian; Deb, Kalyanmoy IEEE Access, Vol. 8 https://doi.org/10.1109/ACCESS.2020.2990567	journal	January 2020
Random decision forests Proceedings of 3rd International Conference on Document Analysis and Recognition https://doi.org/10.1109/ICDAR.1995.598994	conference	January 1995
Particle swarm optimization Kennedy, J.; Eberhart, R. Proceedings of ICNN'95 - International Conference on Neural Networks https://doi.org/10.1109/ICNN.1995.488968	conference	January 1995
ARChemist: Autonomous Robotic Chemistry System Architecture Fakhruldeen, Hatem; Pizzuto, Gabriella; Glowacki, Jakub 2022 International Conference on Robotics and Automation (ICRA) https://doi.org/10.1109/ICRA46639.2022.9811996	conference	May 2022
MVTrans: Multi-View Perception of Transparent Objects Wang, Yi Ru; Zhao, Yuchi; Xu, Haoping 2023 IEEE International Conference on Robotics and Automation (ICRA) https://doi.org/10.1109/ICRA48891.2023.10161089	conference	May 2023
MuJoCo: A physics engine for model-based control Todorov, Emanuel; Erez, Tom; Tassa, Yuval 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems https://doi.org/10.1109/IROS.2012.6386109	conference	October 2012
Robotic Powder Grinding with a Soft Jig for Laboratory Automation in Material Science Nakajima, Yusaku; Hamaya, Masashi; Suzuki, Yuta 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) https://doi.org/10.1109/IROS47612.2022.9981081	conference	October 2022
Core Processes in Intelligent Robotic Lab Assistants: Flexible Liquid Handling Knobbe, Dennis; Zwirnmann, Henning; Eckhoff, Moritz 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) https://doi.org/10.1109/IROS47612.2022.9981636	conference	October 2022
Learning Robotic Powder Weighing from Simulation for Laboratory Automation Kadokawa, Yuki; Hamaya, Masashi; Tanaka, Kazutoshi 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) https://doi.org/10.1109/IROS55552.2023.10342463	conference	October 2023
Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments Mittal, Mayank; Yu, Calvin; Yu, Qinxi IEEE Robotics and Automation Letters, Vol. 8, Issue 6 https://doi.org/10.1109/LRA.2023.3270034	journal	June 2023
Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey Zhao, Wenshuai; Queralta, Jorge Pena; Westerlund, Tomi 2020 IEEE Symposium Series on Computational Intelligence (SSCI) https://doi.org/10.1109/SSCI47803.2020.9308468	conference	December 2020
Development of a Robotic System for Automatic Organic Chemistry Synthesis Lim, Joyce Xin-Yan; Leow, Dasheng; Pham, Quang-Cuong IEEE Transactions on Automation Science and Engineering, Vol. 18, Issue 4 https://doi.org/10.1109/TASE.2020.3036055	journal	October 2021
On the Choice of Smoothing Parameters for Parzen Estimators of Probability Density Functions IEEE Transactions on Computers, Vol. C-25, Issue 11 https://doi.org/10.1109/TC.1976.1674577	journal	November 1976
ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems Knowles, J. IEEE Transactions on Evolutionary Computation, Vol. 10, Issue 1 https://doi.org/10.1109/TEVC.2005.851274	journal	February 2006
An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints Deb, Kalyanmoy; Jain, Himanshu IEEE Transactions on Evolutionary Computation, Vol. 18, Issue 4 https://doi.org/10.1109/TEVC.2013.2281535	journal	August 2014
Nearest neighbor pattern classification Cover, T.; Hart, P. IEEE Transactions on Information Theory, Vol. 13, Issue 1, p. 21-27 https://doi.org/10.1109/TIT.1967.1053964	journal	January 1967
A Survey on Transfer Learning Pan, Sinno Jialin; Yang, Qiang IEEE Transactions on Knowledge and Data Engineering, Vol. 22, Issue 10 https://doi.org/10.1109/TKDE.2009.191	journal	October 2010
FDA Modernization Act 2.0 allows for alternatives to animal testing Han, Jason J. Artificial Organs, Vol. 47, Issue 3 https://doi.org/10.1111/aor.14503	journal	February 2023
Principles of early drug discovery: Principles of early drug discovery Hughes, Jp; Rees, S.; Kalindjian, Sb British Journal of Pharmacology, Vol. 162, Issue 6 https://doi.org/10.1111/j.1476-5381.2010.01127.x	journal	February 2011
Autonomous Structural Visual Inspection Using Region‐Based Deep Learning for Detecting Multiple Damage Types Cha, Young‐Jin; Choi, Wooram; Suh, Gahyun Computer-Aided Civil and Infrastructure Engineering, Vol. 33, Issue 9 https://doi.org/10.1111/mice.12334	journal	November 2017
sMF-BO-2CoGP: A Sequential Multi-Fidelity Constrained Bayesian Optimization Framework for Design Applications Tran, Anh; Wildey, Tim; McCann, Scott Journal of Computing and Information Science in Engineering, Vol. 20, Issue 3 https://doi.org/10.1115/1.4046697	journal	April 2020
Toward continuous-wave operation of organic semiconductor lasers Sandanayaka, Atula S. D.; Matsushima, Toshinori; Bencheikh, Fatima Science Advances, Vol. 3, Issue 4 https://doi.org/10.1126/sciadv.1602570	journal	April 2017
A curious formulation robot enables the discovery of a novel protocell behavior Grizou, Jonathan; Points, Laurie J.; Sharma, Abhishek Science Advances, Vol. 6, Issue 5 https://doi.org/10.1126/sciadv.aay4237	journal	January 2020
A Bayesian experimental autonomous researcher for mechanical design Gongora, Aldair E.; Xu, Bowen; Perry, Wyatt Science Advances, Vol. 6, Issue 15 https://doi.org/10.1126/sciadv.aaz1708	journal	April 2020
Self-driving laboratory for accelerated discovery of thin-film materials MacLeod, B. P.; Parlane, F. G. L.; Morrissey, T. D. Science Advances, Vol. 6, Issue 20 https://doi.org/10.1126/sciadv.aaz8867	journal	May 2020
Accelerated discovery of 3D printing materials using data-driven multiobjective optimization Erps, Timothy; Foshey, Michael; Luković, Mina Konaković Science Advances, Vol. 7, Issue 42 https://doi.org/10.1126/sciadv.abf7435	journal	October 2021
Autonomous materials synthesis via hierarchical active learning of nonequilibrium phase diagrams Ament, Sebastian; Amsler, Maximilian; Sutherland, Duncan R. Science Advances, Vol. 7, Issue 51 https://doi.org/10.1126/sciadv.abg4930	journal	December 2021
An artificial intelligence enabled chemical synthesis robot for exploration and optimization of nanomaterials Jiang, Yibin; Salley, Daniel; Sharma, Abhishek Science Advances, Vol. 8, Issue 40 https://doi.org/10.1126/sciadv.abo2626	journal	October 2022
AI-driven robotic chemist for autonomous synthesis of organic molecules Ha, Taesin; Lee, Dongseon; Kwon, Youngchun Science Advances, Vol. 9, Issue 44 https://doi.org/10.1126/sciadv.adj0461	journal	November 2023
The Automation of Science King, Ross D.; Rowland, Jem; Oliver, Stephen G. Science, Vol. 324, Issue 5923 https://doi.org/10.1126/science.1165620	journal	April 2009
Automation in the Laboratory Spinrad, R. J. Science, Vol. 158, Issue 3797 https://doi.org/10.1126/science.158.3797.55	journal	October 1967
A Chemically Functionalizable Nanoporous Material [Cu₃(TMA)₂(H₂O)₃]_n Chui, Stephen S.-Y.; Lo, Samuel M.-F.; Charmant, Jonathan P. H. Science, Vol. 283, Issue 5405, p. 1148-1150 https://doi.org/10.1126/science.283.5405.1148	journal	February 1999
Robotic Laboratory Automation Boyd, James Science, Vol. 295, Issue 5554 https://doi.org/10.1126/science.295.5554.517	journal	January 2002
Low trap-state density and long carrier diffusion in organolead trihalide perovskite single crystals Shi, D.; Adinolfi, V.; Comin, R. Science, Vol. 347, Issue 6221 https://doi.org/10.1126/science.aaa2725	journal	January 2015
Synthesis of many different types of organic small molecules using one automated process Li, J.; Ballmer, S. G.; Gillis, E. P. Science, Vol. 347, Issue 6227 https://doi.org/10.1126/science.aaa5414	journal	March 2015
On-demand continuous-flow production of pharmaceuticals in a compact, reconfigurable system Adamo, A.; Beingessner, R. L.; Behnam, M. Science, Vol. 352, Issue 6281 https://doi.org/10.1126/science.aaf1337	journal	March 2016
Reconfigurable system for automated optimization of diverse chemical reactions Bédard, Anne-Catherine; Adamo, Andrea; Aroh, Kosi C. Science, Vol. 361, Issue 6408 https://doi.org/10.1126/science.aat0650	journal	September 2018
Inverse molecular design using machine learning: Generative models for matter engineering Sanchez-Lengeling, Benjamin; Aspuru-Guzik, Alán Science, Vol. 361, Issue 6400 https://doi.org/10.1126/science.aat2663	journal	July 2018
Organic synthesis in a modular robotic system driven by a chemical programming language Steiner, Sebastian; Wolf, Jakob; Glatzel, Stefan Science, Vol. 363, Issue 6423 https://doi.org/10.1126/science.aav2211	journal	November 2018
A robotic platform for flow synthesis of organic compounds informed by AI planning Coley, Connor W.; Thomas, Dale A.; Lummiss, Justin A. M. Science, Vol. 365, Issue 6453 https://doi.org/10.1126/science.aax1566	journal	August 2019
A universal system for digitization and automatic execution of the chemical synthesis literature Mehr, S. Hessam M.; Craven, Matthew; Leonov, Artem I. Science, Vol. 370, Issue 6512 https://doi.org/10.1126/science.abc2986	journal	October 2020
Open science discovery of potent noncovalent SARS-CoV-2 main protease inhibitors Boby, Melissa L.; Fearon, Daren; Ferla, Matteo Science, Vol. 382, Issue 6671 https://doi.org/10.1126/science.abo7201	journal	November 2023
Closed-loop optimization of general reaction conditions for heteroaryl Suzuki-Miyaura coupling Angello, Nicholas H.; Rathore, Vandana; Beker, Wiktor Science, Vol. 378, Issue 6618 https://doi.org/10.1126/science.adc8743	journal	October 2022
Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back Koscher, Brent A.; Canty, Richard B.; McDonald, Matthew A. Science, Vol. 382, Issue 6677 https://doi.org/10.1126/science.adi1407	journal	December 2023
Automated self-optimization, intensification, and scale-up of photocatalysis in flow Slattery, Aidan; Wen, Zhenghui; Tenblad, Pauline Science, Vol. 383, Issue 6681 https://doi.org/10.1126/science.adj1817	journal	January 2024
Delocalized, asynchronous, closed-loop discovery of organic laser emitters Strieth-Kalthoff, Felix; Hao, Han; Rathore, Vandana Science, Vol. 384, Issue 6697 https://doi.org/10.1126/science.adk9227	journal	May 2024
SNOBFIT -- Stable Noisy Optimization by Branch and Fit Huyer, Waltraud; Neumaier, Arnold ACM Transactions on Mathematical Software, Vol. 35, Issue 2 https://doi.org/10.1145/1377612.1377613	journal	July 2008
Pyevolve Perone, Christian S. ACM SIGEVOlution, Vol. 4, Issue 1 https://doi.org/10.1145/1656395.1656397	journal	November 2009
Jubilee: An Extensible Machine for Multi-tool Fabrication Vasquez, Joshua; Twigg-Smith, Hannah; Tran O'Leary, Jasper Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems https://doi.org/10.1145/3313831.3376425	conference	April 2020
3D Printed Microfluidics Nielsen, Anna V.; Beauchamp, Michael J.; Nordin, Gregory P. Annual Review of Analytical Chemistry, Vol. 13, Issue 1 https://doi.org/10.1146/annurev-anchem-091619-102649	journal	June 2020
Combinatorial Investigations of Si-M (M = Cr + Ni, Fe, Mn) Thin Film Negative Electrode Materials Fleischauer, M. D.; Topple, J. M.; Dahn, J. R. Electrochemical and Solid-State Letters, Vol. 8, Issue 2 https://doi.org/10.1149/1.1850395	journal	January 2005
High-Throughput Electrochemical Characterization of Aqueous Organic Redox Flow Battery Active Material Fell, Eric M.; Aziz, Michael J. Journal of The Electrochemical Society, Vol. 170, Issue 10 https://doi.org/10.1149/1945-7111/acfcde	journal	October 2023
A Simple Statistical Parameter for Use in Evaluation and Validation of High Throughput Screening Assays Zhang, J. -H. Journal of Biomolecular Screening, Vol. 4, Issue 2 https://doi.org/10.1177/108705719900400206	journal	April 1999
FINDUS: An Open-Source 3D Printable Liquid-Handling Workstation for Laboratory Automation in Life Sciences Barthels, Fabian; Barthels, Ulrich; Schwickert, Marvin SLAS Technology, Vol. 25, Issue 2 https://doi.org/10.1177/2472630319877374	journal	April 2020
Towards Robot Scientists for autonomous scientific discovery Sparkes, Andrew; Aubrey, Wayne; Byrne, Emma Automated Experimentation, Vol. 2, Issue 1 https://doi.org/10.1186/1759-4499-2-1	journal	January 2010
Molecular de-novo design through deep reinforcement learning Olivecrona, Marcus; Blaschke, Thomas; Engkvist, Ola Journal of Cheminformatics, Vol. 9, Issue 1 https://doi.org/10.1186/s13321-017-0235-x	journal	September 2017
Mordred: a molecular descriptor calculator Moriwaki, Hirotomo; Tian, Yu-Shi; Kawashita, Norihito Journal of Cheminformatics, Vol. 10, Issue 1 https://doi.org/10.1186/s13321-018-0258-y	journal	February 2018
One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome Capecchi, Alice; Probst, Daniel; Reymond, Jean-Louis Journal of Cheminformatics, Vol. 12, Issue 1 https://doi.org/10.1186/s13321-020-00445-4	journal	June 2020
Molecular representations in AI-driven drug discovery: a review and practical guide David, Laurianne; Thakkar, Amol; Mercado, Rocío Journal of Cheminformatics, Vol. 12, Issue 1 https://doi.org/10.1186/s13321-020-00460-5	journal	September 2020
Chemical reaction network knowledge graphs: the OntoRXN ontology Garay-Ruiz, Diego; Bo, Carles Journal of Cheminformatics, Vol. 14, Issue 1 https://doi.org/10.1186/s13321-022-00610-x	journal	May 2022
machine. Friedman, Jerome H. The Annals of Statistics, Vol. 29, Issue 5 https://doi.org/10.1214/aos/1013203451	journal	October 2001
Simplex optimization of reaction conditions with an automated system. Matsuda, Rieko; Ishibashi, Mumio; Takeda, Yasushi Chemical and Pharmaceutical Bulletin, Vol. 36, Issue 9 https://doi.org/10.1248/cpb.36.3512	journal	January 1988
Simplex Optimization of Reaction Condition Using Laboratory Robotic System Matsuda, Rieko; Ishibashi, Mumio; Uchiyama, Mitsuru YAKUGAKU ZASSHI, Vol. 107, Issue 9 https://doi.org/10.1248/yakushi1947.107.9_683	journal	January 1987
Sustainable data analysis with Snakemake Mölder, Felix; Jablonski, Kim Philipp; Letcher, Brice F1000Research, Vol. 10 https://doi.org/10.12688/f1000research.29032.1	journal	January 2021
Real-Time Feedback Control Using Online Attenuated Total Reflection Fourier Transform Infrared (ATR FT-IR) Spectroscopy for Continuous Flow Optimization and Process Knowledge Skilton, Ryan A.; Parrott, Andrew J.; George, Michael W. Applied Spectroscopy, Vol. 67, Issue 10 https://doi.org/10.1366/13-06999	journal	October 2013
Open Labware: 3-D Printing Your Own Lab Equipment Baden, Tom; Chagas, Andre Maia; Gage, Greg PLOS Biology, Vol. 13, Issue 3 https://doi.org/10.1371/journal.pbio.1002086	journal	March 2015
A One Pot, One Step, Precision Cloning Method with High Throughput Capability Engler, Carola; Kandzia, Romy; Marillonnet, Sylvestre PLoS ONE, Vol. 3, Issue 11, Article No. e3647 https://doi.org/10.1371/journal.pone.0003647	journal	November 2008
ChemOS: An orchestration software to democratize autonomous discovery Roch, Loïc M.; Häse, Florian; Kreisbeck, Christoph PLOS ONE, Vol. 15, Issue 4 https://doi.org/10.1371/journal.pone.0229862	journal	April 2020
The Effect of Self-Optimisation Targets on the Methylation of Alcohols Using Dimethyl Carbonate in Supercritical CO2 Jumbam, Denis N.; Skilton, Ryan A.; Parrott, Andrew J. Journal of Flow Chemistry, Vol. 2, Issue 1 https://doi.org/10.1556/jfchem.2012.00019	journal	January 2012
Opportunities and challenges for first-principles materials design and applications to Li battery materials Ceder, Gerbrand MRS Bulletin, Vol. 35, Issue 9 https://doi.org/10.1557/mrs2010.681	journal	September 2010
Toward autonomous additive manufacturing: Bayesian optimization on a 3D printer Deneault, James R.; Chang, Jorge; Myung, Jay MRS Bulletin, Vol. 46, Issue 7 https://doi.org/10.1557/s43577-021-00051-1	journal	April 2021
Mathematical nuances of Gaussian process-driven autonomous experimentation Noack, Marcus M.; Reyes, Kristofer G. MRS Bulletin, Vol. 48, Issue 2, p. 153-163 https://doi.org/10.1557/s43577-023-00478-8	journal	February 2023
Application of simplex optimization in the development of an automated online preconcentration system for manganese determination Santos, Queila O. dos; Novaes, Cléber G.; Bezerra, Marcos A. Journal of the Brazilian Chemical Society, Vol. 21, Issue 12 https://doi.org/10.1590/S0103-50532010001200022	journal	December 2010
eLabFTW: An open source laboratory notebook for research labs CARPi, Nicolas; Minges, Alexander; Piel, Matthieu The Journal of Open Source Software, Vol. 2, Issue 12 https://doi.org/10.21105/joss.00146	journal	April 2017
funsies: A minimalist, distributed and dynamic workflow engine Lavigne, Cyrille; Aspuru-Guzik, Alán Journal of Open Source Software, Vol. 6, Issue 66 https://doi.org/10.21105/joss.03274	journal	October 2021
Development of an Automatic pH Adjustment Instrument for the Preparation of Analytical Samples Prior to Solid Phase Extraction Zhu, Yanbei; Nishigori, Sayaka; Shimura, Nobuaki Analytical Sciences, Vol. 36, Issue 5 https://doi.org/10.2116/analsci.19SBN03	journal	February 2020
A Gentle Introduction to Graph Neural Networks Sanchez-Lengeling, Benjamin; Reif, Emily; Pearce, Adam Distill, Vol. 6, Issue 8 https://doi.org/10.23915/distill.00033	journal	August 2021
Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms Bergstra, James; Yamins, Dan; Cox, David Proceedings of the Python in Science Conference https://doi.org/10.25080/Majora-8b375195-003	conference	January 2013
The Evolution of Flow Chemistry: An Opinion on Factors Driving Innovation Bourne, Samuel L.; Amann, Franz; Ley, Steven V. CHIMIA, Vol. 77, Issue 5 https://doi.org/10.2533/chimia.2023.288	journal	May 2023
Self-Driving Laboratories for Chemistry and Materials Science Tom, Gary; Schmid, Stefan P.; Baird, Sterling G. https://doi.org/10.26434/chemrxiv-2024-rj946-v2	preprint	June 2024
Fusion of neural networks with fuzzy logic and genetic algorithm Cho, Sung-Bae Integrated Computer-Aided Engineering, Vol. 9, Issue 4 https://doi.org/10.3233/ICA-2002-9405	journal	November 2002
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models Polykovskiy, Daniil; Zhebrak, Alexander; Sanchez-Lengeling, Benjamin Frontiers in Pharmacology, Vol. 11 https://doi.org/10.3389/fphar.2020.565644	journal	December 2020
EvoBot: An Open-Source, Modular, Liquid Handling Robot for Scientific Experiments Faiña, Andres; Nejati, Brian; Stoy, Kasper Applied Sciences, Vol. 10, Issue 3 https://doi.org/10.3390/app10030814	journal	January 2020
Multi-Objective Optimization Applications in Chemical Process Engineering: Tutorial and Review Rangaiah, Gade Pandu; Feng, Zemin; Hoadley, Andrew F. Processes, Vol. 8, Issue 5 https://doi.org/10.3390/pr8050508	journal	April 2020
Self-optimisation and model-based design of experiments for developing a C–H activation flow process Echtermeyer, Alexander; Amar, Yehia; Zakrzewski, Jacek Beilstein Journal of Organic Chemistry, Vol. 13 https://doi.org/10.3762/bjoc.13.18	journal	January 2017
Anova Girden, Ellen R. https://doi.org/10.4135/9781412983419	book	January 1992
Algorithms for multi-armed bandit problems Kuleshov, Volodymyr; Precup, Doina arXiv https://doi.org/10.48550/arXiv.1402.6028	preprint	January 2014
Illuminating search spaces by mapping elites Mouret, Jean-Baptiste; Clune, Jeff arXiv https://doi.org/10.48550/arXiv.1504.04909	preprint	January 2015
Convolutional Networks on Graphs for Learning Molecular Fingerprints Duvenaud, David; Maclaurin, Dougal; Aguilera-Iparraguirre, Jorge arXiv https://doi.org/10.48550/arXiv.1509.09292	preprint	January 2015
A Unified Approach to Interpreting Model Predictions Lundberg, Scott; Lee, Su-In arXiv https://doi.org/10.48550/arXiv.1705.07874	preprint	January 2017
MolGAN: An implicit generative model for small molecular graphs De Cao, Nicola; Kipf, Thomas arXiv https://doi.org/10.48550/arXiv.1805.11973	text	January 2018
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly Kandasamy, Kirthevasan; Vysyaraju, Karun Raju; Neiswanger, Willie arXiv https://doi.org/10.48550/arXiv.1903.06694	preprint	January 2019
Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules Sanchez-Lengeling, Benjamin; Wei, Jennifer N.; Lee, Brian K. arXiv https://doi.org/10.48550/arXiv.1910.10685	preprint	January 2019
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning Yu, Tianhe; Quillen, Deirdre; He, Zhanpeng arXiv https://doi.org/10.48550/arXiv.1910.10897	preprint	January 2019
Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization Daulton, Samuel; Balandat, Maximilian; Bakshy, Eytan arXiv https://doi.org/10.48550/arXiv.2006.05078	text	January 2020
HEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisation Cowen-Rivers, Alexander I.; Lyu, Wenlong; Tutunov, Rasul arXiv https://doi.org/10.48550/arXiv.2012.03826	preprint	January 2020
E(n) Equivariant Graph Neural Networks Satorras, Victor Garcia; Hoogeboom, Emiel; Welling, Max arXiv https://doi.org/10.48550/arXiv.2102.09844	preprint	January 2021
Gemini: Dynamic Bias Correction for Autonomous Experimentation and Molecular Simulation Hickman, Riley J.; Häse, Florian; Roch, Loïc M. arXiv https://doi.org/10.48550/arXiv.2103.03391	preprint	January 2021
Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement Daulton, Samuel; Balandat, Maximilian; Bakshy, Eytan arXiv https://doi.org/10.48550/arXiv.2105.08195	preprint	January 2021
Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation Bengio, Emmanuel; Jain, Moksh; Korablyov, Maksym arXiv https://doi.org/10.48550/arXiv.2106.04399	preprint	January 2021
iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks Li, Chengshu; Xia, Fei; Martín-Martín, Roberto arXiv https://doi.org/10.48550/arXiv.2108.03272	preprint	January 2021
Seeing Glass: Joint Point Cloud and Depth Completion for Transparent Objects Xu, Haoping; Wang, Yi Ru; Eppel, Sagi arXiv https://doi.org/10.48550/arXiv.2110.00087	preprint	January 2021
RB2: Robotic Manipulation Benchmarking with a Twist Dasari, Sudeep; Wang, Jianren; Hong, Joyce arXiv https://doi.org/10.48550/arXiv.2203.08098	preprint	January 2022
Recipe for a General, Powerful, Scalable Graph Transformer Rampášek, Ladislav; Galkin, Mikhail; Dwivedi, Vijay Prakash arXiv https://doi.org/10.48550/arXiv.2205.12454	preprint	January 2022
Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design Nigam, AkshatKumar; Pollice, Robert; Tom, Gary arXiv https://doi.org/10.48550/arXiv.2209.12487	preprint	January 2022
Accelerating Laboratory Automation Through Robot Skill Learning For Sample Scraping Pizzuto, Gabriella; Wang, Hetong; Fakhruldeen, Hatem arXiv https://doi.org/10.48550/arXiv.2209.14875	preprint	January 2022
Galactica: A Large Language Model for Science Taylor, Ross; Kardas, Marcin; Cucurull, Guillem arXiv https://doi.org/10.48550/arXiv.2211.09085	preprint	January 2022
GAUCHE: A Library for Gaussian Processes in Chemistry Griffiths, Ryan-Rhys; Klarner, Leo; Moss, Henry B. arXiv https://doi.org/10.48550/arXiv.2212.04450	preprint	January 2022
Chemistry Lab Automation via Constrained Task and Motion Planning Yoshikawa, Naruki; Li, Andrew Zou; Darvish, Kourosh arXiv https://doi.org/10.48550/arXiv.2212.09672	preprint	January 2022
Active Learning and Bayesian Optimization: a Unified Perspective to Learn with a Goal Di Fiore, Francesco; Nardelli, Michela; Mainini, Laura arXiv https://doi.org/10.48550/arXiv.2303.01560	text	January 2023
FluidLab: A Differentiable Environment for Benchmarking Complex Fluid Manipulation Xian, Zhou; Zhu, Bo; Xu, Zhenjia arXiv https://doi.org/10.48550/arXiv.2303.02346	preprint	January 2023
Navigating phase diagram complexity to guide robotic inorganic materials synthesis Chen, Jiadong; Cross, Samuel R.; Miara, Lincoln J. arXiv https://doi.org/10.48550/arXiv.2304.00743	text	January 2023
A Critical Evaluation of a Self-Driving Laboratory for the Optimization of Electrodeposited Earth-Abundant Mixed-Metal Oxide Catalysts for the Oxygen Evolution Reaction (OER) Fatehi, Erfan; Thadani, Manish; Birsan, Gabriel arXiv https://doi.org/10.48550/arXiv.2305.12541	preprint	January 2023
ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry Beeler, Chris; Subramanian, Sriram Ganapathi; Sprague, Kyle arXiv https://doi.org/10.48550/arXiv.2305.14177	preprint	January 2023
What indeed can GPT models do in chemistry? A comprehensive benchmark on eight tasks Guo, Taicheng; Guo, Kehan; Nan, Bozhao arXiv https://doi.org/10.48550/arXiv.2305.18365	preprint	January 2023
Towards a Modular Architecture for Science Factories Vescovi, Rafael; Ginsburg, Tobias; Hippe, Kyle arXiv https://doi.org/10.48550/arXiv.2308.09793	preprint	January 2023
Modular, Multi-Robot Integration of Laboratories: An Autonomous Solid-State Workflow for Powder X-Ray Diffraction Lunt, Amy. M.; Fakhruldeen, Hatem; Pizzuto, Gabriella arXiv https://doi.org/10.48550/arXiv.2309.00544	preprint	January 2023
Physics-driven discovery and bandgap engineering of hybrid perovskites Sanchez, Sheryl L.; Foadian, Elham; Ziatdinov, Maxim arXiv https://doi.org/10.48550/arXiv.2310.06583	preprint	January 2023
ORGANA: A Robotic Assistant for Automated Chemistry Experimentation and Characterization Darvish, Kourosh; Skreta, Marta; Zhao, Yuchi arXiv https://doi.org/10.48550/arXiv.2401.06949	preprint	January 2024
Co-orchestration of Multiple Instruments to Uncover Structure-Property Relationships in Combinatorial Libraries Slautin, Boris N.; Pratiush, Utkarsh; Ivanov, Ilia N. arXiv https://doi.org/10.48550/arXiv.2402.02198	text	January 2024
BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulation Li, Chengshu; Zhang, Ruohan; Wong, Josiah arXiv https://doi.org/10.48550/arXiv.2403.09227	preprint	January 2024
AlabOS: A Python-based Reconfigurable Workflow Management Framework for Autonomous Laboratories Fei, Yuxing; Rendy, Bernardus; Kumar, Rishi arXiv https://doi.org/10.48550/arXiv.2405.13930	preprint	January 2024
Generation and human-expert evaluation of interesting research ideas using knowledge graphs and large language models Gu, Xuemei; Krenn, Mario arXiv https://doi.org/10.48550/arXiv.2405.17044	preprint	January 2024
Methods of conjugate gradients for solving linear systems Hestenes, M. R.; Stiefel, E. Journal of Research of the National Bureau of Standards, Vol. 49, Issue 6 https://doi.org/10.6028/jres.049.044	journal	December 1952
Chemical reactions from US patents (1976-Sep2016) Lowe, Daniel figshare https://doi.org/10.6084/M9.FIGSHARE.5104873.V1	dataset	January 2017
Robotic search for optimal cell culture in regenerative medicine Kanda, Genki N.; Tsuzuki, Taku; Terada, Motoki eLife, Vol. 11 https://doi.org/10.7554/eLife.77007	journal	June 2022
Indication of current-injection lasing from an organic semiconductor Sandanayaka, Atula S. D.; Matsushima, Toshinori; Bencheikh, Fatima Applied Physics Express, Vol. 12, Issue 6 https://doi.org/10.7567/1882-0786/ab1b90	journal	May 2019

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Algorithms
Chemistry
Liquids
Materials
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Software

Title: Self-Driving Laboratories for Chemistry and Materials Science

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