Autonomous experimentation systems for materials development: A community perspective

Stach, Eric; DeCost, Brian; Kusne, A. Gilad; Hattrick-Simpers, Jason; Brown, Keith A.; Reyes, Kristofer G.; Schrier, Joshua; Billinge, Simon; Buonassisi, Tonio; Foster, Ian; Gomes, Carla P.; Gregoire, John M.; Mehta, Apurva; Montoya, Joseph; Olivetti, Elsa; Park, Chiwoo; Rotenberg, Eli; Saikin, Semion K.; Smullin, Sylvia; Stanev, Valentin; Maruyama, Benji

doi:10.1016/j.matt.2021.06.036

Autonomous experimentation systems for materials development: A community perspective

Journal Article · Wed Sep 01 00:00:00 EDT 2021 · Matter (Online)

DOI:https://doi.org/10.1016/j.matt.2021.06.036· OSTI ID:1823639

Stach, Eric ^[1]; DeCost, Brian ^[2]; Kusne, A. Gilad ^[2]; Hattrick-Simpers, Jason ^[2]; Brown, Keith A. ^[3]; Reyes, Kristofer G. ^[4]; Schrier, Joshua ^[5]; Billinge, Simon ^[6]; Buonassisi, Tonio ^[7]; Foster, Ian ^[8]; Gomes, Carla P. ^[9]; Gregoire, John M. ^[10]; Mehta, Apurva ^[11]; Montoya, Joseph ^[12]; Olivetti, Elsa ^[7]; Park, Chiwoo ^[13]; Rotenberg, Eli ^[14]; Saikin, Semion K. ^[15]; Smullin, Sylvia ^[16]; Stanev, Valentin ^[17] more »

Univ. of Pennsylvania, Philadelphia, PA (United States)
National Inst. of Standards and Technology (NIST), Gaithersburg, MD (United States)
Boston Univ., MA (United States)
Univ. of Buffalo, NY (United States)
Fordham Univ., The Bronx, NY (United States)
Columbia Univ., New York, NY (United States); Brookhaven National Lab. (BNL), Upton, NY (United States)
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Argonne National Lab. (ANL), Argonne, IL (United States); Univ. of Chicago, IL (United States)
Cornell Univ., Ithaca, NY (United States)
California Institute of Technology (CalTech), Pasadena, CA (United States)
SLAC National Accelerator Lab., Menlo Park, CA (United States)
Toyota Research Institute, Los Altos, CA (United States)
Florida State Univ., Tallahassee, FL (United States)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Kebotix, Inc., Cambridge, MA (United Staets)
The Moonshot Factory, Mountain View, CA (United States)
Univ. of Maryland, College Park, MD (United States)
Air Force Research Lab. (AFRL), Wright-Patterson AFB, OH (United States)

Solutions to many of the world's problems depend upon materials research and development. However, advanced materials can take decades to discover and decades more to fully deploy. Humans and robots have begun to partner to advance science and technology orders of magnitude faster than humans do today through the development and exploitation of closed-loop, autonomous experimentation systems. This review discusses the specific challenges and opportunities related to materials discovery and development that will emerge from this new paradigm. Our perspective incorporates input from stakeholders in academia, industry, government laboratories, and funding agencies. We outline the current status, barriers, and needed investments, culminating with a vision for the path forward. We intend the article to spark interest in this emerging research area and to motivate potential practitioners by illustrating early successes. We also aspire to encourage a creative reimagining of the next generation of materials science infrastructure. To this end, we frame future investments in materials science and technology, hardware and software infrastructure, artificial intelligence and autonomy methods, and critical workforce development for autonomous research.

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: Camille and Henry Dreyfus Foundation; Defense Advanced Research Projects Agency; National Science Foundation (NSF); US Air Force Office of Scientific Research (AFOSR); USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: AC02-05CH11231; AC02-06CH11357

OSTI ID:: 1823639

Journal Information:: Matter (Online), Journal Name: Matter (Online) Journal Issue: 9 Vol. 4; ISSN 2590-2385

Publisher:: Cell Press/ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (116)

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
Toward “On‐Demand” Materials Synthesis and Scientific Discovery through Intelligent Robots Li, Jiagen; Tu, Yuxiao; Liu, Rulin Advanced Science, Vol. 7, Issue 7 https://doi.org/10.1002/advs.201901957	journal	February 2020
Machine learning and artificial neural network accelerated computational discoveries in materials science Hong, Yang; Hou, Bo; Jiang, Hengle WIREs Computational Molecular Science, Vol. 10, Issue 3 https://doi.org/10.1002/wcms.1450	journal	November 2019
A Survey on Deep Transfer Learning Tan, Chuanqi; Sun, Fuchun; Kong, Tao Artificial Neural Networks and Machine Learning – ICANN 2018 https://doi.org/10.1007/978-3-030-01424-7_27	book	January 2018
The origins of kriging Cressie, Noel Mathematical Geology, Vol. 22, Issue 3 https://doi.org/10.1007/BF00889887	journal	April 1990
Prediction of Nanoscale Friction for Two-Dimensional Materials Using a Machine Learning Approach Sattari Baboukani, Behnoosh; Ye, Zhijiang; G. Reyes, Kristofer Tribology Letters, Vol. 68, Issue 2 https://doi.org/10.1007/s11249-020-01294-w	journal	April 2020
Integrated computational materials engineering: A new paradigm for the global materials profession Allison, John; Backman, Dan; Christodoulou, Leo JOM, Vol. 58, Issue 11 https://doi.org/10.1007/s11837-006-0223-5	journal	November 2006
The Materials Data Facility: Data Services to Advance Materials Science Research Blaiszik, B.; Chard, K.; Pruyne, J. JOM, Vol. 68, Issue 8 https://doi.org/10.1007/s11837-016-2001-3	journal	July 2016
PRISMS: An Integrated, Open-Source Framework for Accelerating Predictive Structural Materials Science Aagesen, L. K.; Adams, J. F.; Allison, J. E. JOM, Vol. 70, Issue 10 https://doi.org/10.1007/s11837-018-3079-6	journal	August 2018
A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality Bessa, M. A.; Bostanabad, R.; Liu, Z. Computer Methods in Applied Mechanics and Engineering, Vol. 320 https://doi.org/10.1016/j.cma.2017.03.037	journal	June 2017
Materials Acceleration Platforms: On the way to autonomous experimentation Flores-Leonar, Martha M.; Mejía-Mendoza, Luis M.; Aguilar-Granda, Andrés Current Opinion in Green and Sustainable Chemistry, Vol. 25 https://doi.org/10.1016/j.cogsc.2020.100370	journal	October 2020
AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations Curtarolo, Stefano; Setyawan, Wahyu; Wang, Shidong Computational Materials Science, Vol. 58 https://doi.org/10.1016/j.commatsci.2012.02.002	journal	June 2012
Multi-fidelity machine learning models for accurate bandgap predictions of solids Pilania, G.; Gubernatis, J. E.; Lookman, T. Computational Materials Science, Vol. 129 https://doi.org/10.1016/j.commatsci.2016.12.004	journal	March 2017
Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows Mathew, Kiran; Montoya, Joseph H.; Faghaninia, Alireza Computational Materials Science, Vol. 139 https://doi.org/10.1016/j.commatsci.2017.07.030	journal	November 2017
The Materials Genome Initiative, the interplay of experiment, theory and computation de Pablo, Juan J.; Jones, Barbara; Kovacs, Cora Lind Current Opinion in Solid State and Materials Science, Vol. 18, Issue 2 https://doi.org/10.1016/j.cossms.2014.02.003	journal	April 2014
Statistical inference and adaptive design for materials discovery Lookman, Turab; Balachandran, Prasanna V.; Xue, Dezhen Current Opinion in Solid State and Materials Science, Vol. 21, Issue 3 https://doi.org/10.1016/j.cossms.2016.10.002	journal	June 2017
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
Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing Correa-Baena, Juan-Pablo; Hippalgaonkar, Kedar; van Duren, Jeroen Joule, Vol. 2, Issue 8 https://doi.org/10.1016/j.joule.2018.05.009	journal	August 2018
Accelerated Development of Perovskite-Inspired Materials via High-Throughput Synthesis and Machine-Learning Diagnosis Sun, Shijing; Hartono, Noor T. P.; Ren, Zekun D. Joule, Vol. 3, Issue 6 https://doi.org/10.1016/j.joule.2019.05.014	journal	June 2019
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
A data fusion approach to optimize compositional stability of halide perovskites Sun, Shijing; Tiihonen, Armi; Oviedo, Felipe Matter, Vol. 4, Issue 4 https://doi.org/10.1016/j.matt.2021.01.008	journal	April 2021
Local Navigation Strategies for Multi-Robot Exploration: From Simulation to Experimentation with Mini-Robots Tanoto, Andry; Rückert, Ulrich Procedia Engineering, Vol. 41 https://doi.org/10.1016/j.proeng.2012.07.301	journal	January 2012
Closing the Advanced Manufacturing Talent Gap Javdekar, Chitra; Watson, Elizabeth; Kapilow, Valerie Procedia Manufacturing, Vol. 5 https://doi.org/10.1016/j.promfg.2016.08.094	journal	January 2016
Serendipity: Towards a taxonomy and a theory Yaqub, Ohid Research Policy, Vol. 47, Issue 1 https://doi.org/10.1016/j.respol.2017.10.007	journal	February 2018
Strengthening advanced manufacturing innovation ecosystems: The case of Massachusetts Reynolds, Elisabeth B.; Uygun, Yilmaz Technological Forecasting and Social Change, Vol. 136 https://doi.org/10.1016/j.techfore.2017.06.003	journal	November 2018
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
Adaptive Optimization of Chemical Reactions with Minimal Experimental Information Reker, Daniel; Hoyt, Emily A.; Bernardes, Gonçalo J. L. Cell Reports Physical Science, Vol. 1, Issue 11 https://doi.org/10.1016/j.xcrp.2020.100247	journal	November 2020
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
Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices Wang, Anthony Yu-Tung; Murdock, Ryan J.; Kauwe, Steven K. Chemistry of Materials, Vol. 32, Issue 12 https://doi.org/10.1021/acs.chemmater.0c01907	journal	May 2020
Intuition-Enabled Machine Learning Beats the Competition When Joint Human-Robot Teams Perform Inorganic Chemical Experiments Duros, Vasilios; Grizou, Jonathan; Sharma, Abhishek Journal of Chemical Information and Modeling, Vol. 59, Issue 6 https://doi.org/10.1021/acs.jcim.9b00304	journal	April 2019
AIR-Chem: Authentic Intelligent Robotics for Chemistry Li, Jiagen; Lu, Yihua; Xu, Yanheng The Journal of Physical Chemistry A, Vol. 122, Issue 46 https://doi.org/10.1021/acs.jpca.8b10680	journal	November 2018
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
Exploration of Near-Infrared-Emissive Colloidal Multinary Lead Halide Perovskite Nanocrystals Using an Automated Microfluidic Platform Lignos, Ioannis; Morad, Viktoriia; Shynkarenko, Yevhen ACS Nano, Vol. 12, Issue 6 https://doi.org/10.1021/acsnano.8b01122	journal	May 2018
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
Elucidating the Behavior of Nanophotonic Structures through Explainable Machine Learning Algorithms Yeung, Christopher; Tsai, Ju-Ming; King, Brian ACS Photonics, Vol. 7, Issue 8 https://doi.org/10.1021/acsphotonics.0c01067	journal	July 2020
Active Reaction Control of Cu Redox State Based on Real-Time Feedback from In Situ Synchrotron Measurements Rakita, Yevgeny; O’Nolan, Daniel; McAuliffe, Rebecca D. Journal of the American Chemical Society, Vol. 142, Issue 44 https://doi.org/10.1021/jacs.0c09418	journal	October 2020
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
Reproducible, High-Throughput Synthesis of Colloidal Nanocrystals for Optimization in Multidimensional Parameter Space Chan, Emory M.; Xu, Chenxu; Mao, Alvin W. Nano Letters, Vol. 10, Issue 5 https://doi.org/10.1021/nl100669s	journal	May 2010
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
Deep learning LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey Nature, Vol. 521, Issue 7553 https://doi.org/10.1038/nature14539	journal	May 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
Planning chemical syntheses with deep neural networks and symbolic AI Segler, Marwin H. S.; Preuss, Mike; Waller, Mark P. Nature, Vol. 555, Issue 7698 https://doi.org/10.1038/nature25978	journal	March 2018
Combinatorial search of thermoelastic shape-memory alloys with extremely small hysteresis width Cui, Jun; Chu, Yong S.; Famodu, Olugbenga O. Nature Materials, Vol. 5, Issue 4 https://doi.org/10.1038/nmat1593	journal	March 2006
In situ evidence for chirality-dependent growth rates of individual carbon nanotubes Rao, Rahul; Liptak, David; Cherukuri, Tonya Nature Materials, Vol. 11, Issue 3 https://doi.org/10.1038/nmat3231	journal	January 2012
The high-throughput highway to computational materials design Curtarolo, Stefano; Hart, Gus L. W.; Nardelli, Marco Buongiorno Nature Materials, Vol. 12, Issue 3 https://doi.org/10.1038/nmat3568	journal	February 2013
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
Network analysis of synthesizable materials discovery Aykol, Muratahan; Hegde, Vinay I.; Hung, Linda Nature Communications, Vol. 10, Issue 1 https://doi.org/10.1038/s41467-019-10030-5	journal	May 2019
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
Machine learning in materials informatics: recent applications and prospects Ramprasad, Rampi; Batra, Rohit; Pilania, Ghanshyam npj Computational Materials, Vol. 3, Issue 1 https://doi.org/10.1038/s41524-017-0056-5	journal	December 2017
New frontiers for the materials genome initiative de Pablo, Juan J.; Jackson, Nicholas E.; Webb, Michael A. npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0173-4	journal	April 2019
Recent advances and applications of machine learning in solid-state materials science Schmidt, Jonathan; Marques, Mário R. G.; Botti, Silvana npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0221-0	journal	August 2019
Understanding important features of deep learning models for segmentation of high-resolution transmission electron microscopy images Horwath, James P.; Zakharov, Dmitri N.; Mégret, Rémi npj Computational Materials, Vol. 6, Issue 1 https://doi.org/10.1038/s41524-020-00363-x	journal	July 2020
The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design Choudhary, Kamal; Garrity, Kevin F.; Reid, Andrew C. E. npj Computational Materials, Vol. 6, Issue 1 https://doi.org/10.1038/s41524-020-00440-1	journal	November 2020
Accelerating the discovery of materials for clean energy in the era of smart automation Tabor, Daniel P.; Roch, Loïc M.; Saikin, Semion K. Nature Reviews Materials, Vol. 3, Issue 5 https://doi.org/10.1038/s41578-018-0005-z	journal	April 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
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
Phase Segmentation in Atom-Probe Tomography Using Deep Learning-Based Edge Detection Madireddy, Sandeep; Chung, Ding-Wen; Loeffler, Troy Scientific Reports, Vol. 9, Issue 1 https://doi.org/10.1038/s41598-019-56649-8	journal	December 2019
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
Progress and prospects for accelerating materials science with automated and autonomous workflows Stein, Helge S.; Gregoire, John M. Chemical Science, Vol. 10, Issue 42 https://doi.org/10.1039/C9SC03766G	journal	January 2019
Autonomous intelligent agents for accelerated materials discovery Montoya, Joseph H.; Winther, Kirsten T.; Flores, Raul A. Chemical Science, Vol. 11, Issue 32 https://doi.org/10.1039/D0SC01101K	journal	January 2020
Combinatorial discovery of a lead-free morphotropic phase boundary in a thin-film piezoelectric perovskite Fujino, S.; Murakami, M.; Anbusathaiah, V. Applied Physics Letters, Vol. 92, Issue 20 https://doi.org/10.1063/1.2931706	journal	May 2008
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
Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies Green, M. L.; Choi, C. L.; Hattrick-Simpers, J. R. Applied Physics Reviews, Vol. 4, Issue 1 https://doi.org/10.1063/1.4977487	journal	March 2017
Machine learning approaches for the prediction of materials properties Chibani, Siwar; Coudert, François-Xavier APL Materials, Vol. 8, Issue 8 https://doi.org/10.1063/5.0018384	journal	August 2020
Catalyst discovery through megalibraries of nanomaterials Kluender, Edward J.; Hedrick, James L.; Brown, Keith A. Proceedings of the National Academy of Sciences, Vol. 116, Issue 1 https://doi.org/10.1073/pnas.1815358116	journal	December 2018
Closed-loop cycles of experiment design, execution, and learning accelerate systems biology model development in yeast Coutant, Anthony; Roper, Katherine; Trejo-Banos, Daniel Proceedings of the National Academy of Sciences, Vol. 116, Issue 36 https://doi.org/10.1073/pnas.1900548116	journal	August 2019
From DFT to machine learning: recent approaches to materials science–a review Schleder, Gabriel R.; Padilha, Antonio C. M.; Acosta, Carlos Mera Journal of Physics: Materials, Vol. 2, Issue 3 https://doi.org/10.1088/2515-7639/ab084b	journal	May 2019
The NOMAD laboratory: from data sharing to artificial intelligence Draxl, Claudia; Scheffler, Matthias Journal of Physics: Materials, Vol. 2, Issue 3 https://doi.org/10.1088/2515-7639/ab13bb	journal	May 2019
Scientific AI in materials science: a path to a sustainable and scalable paradigm DeCost, Bl; Hattrick-Simpers, Jr; Trautt, Z. Machine Learning: Science and Technology, Vol. 1, Issue 3 https://doi.org/10.1088/2632-2153/ab9a20	journal	July 2020
Machine Ethics: The Design and Governance of Ethical AI and Autonomous Systems [Scanning the Issue] Winfield, Alan F.; Michael, Katina; Pitt, Jeremy Proceedings of the IEEE, Vol. 107, Issue 3 https://doi.org/10.1109/JPROC.2019.2900622	journal	March 2019
Children lose confidence in their potential to “be scientists,” but not in their capacity to “do science” Lei, Ryan F.; Green, Emily R.; Leslie, Sarah‐Jane Developmental Science, Vol. 22, Issue 6 https://doi.org/10.1111/desc.12837	journal	March 2019
Bayesian Hypothesis Testing: a Reference Approach Bernardo, José M.; Rueda, Raúl International Statistical Review, Vol. 70, Issue 3 https://doi.org/10.1111/j.1751-5823.2002.tb00175.x	journal	December 2002
Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments Ren, Fang; Ward, Logan; Williams, Travis Science Advances, Vol. 4, Issue 4 https://doi.org/10.1126/sciadv.aaq1566	journal	April 2018
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
The phase stability network of all inorganic materials Hegde, Vinay I.; Aykol, Muratahan; Kirklin, Scott Science Advances, Vol. 6, Issue 9 https://doi.org/10.1126/sciadv.aay5606	journal	February 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
Explainable and trustworthy artificial intelligence for correctable modeling in chemical sciences Feng, Jinchao; Lansford, Joshua L.; Katsoulakis, Markos A. Science Advances, Vol. 6, Issue 42 https://doi.org/10.1126/sciadv.abc3204	journal	October 2020
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
The collaboratory opportunity Wulf, W. Science, Vol. 261, Issue 5123 https://doi.org/10.1126/science.8346438	journal	August 1993
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
Human-level concept learning through probabilistic program induction Lake, B. M.; Salakhutdinov, R.; Tenenbaum, J. B. Science, Vol. 350, Issue 6266 https://doi.org/10.1126/science.aab3050	journal	December 2015
Making the most of materials computations Thygesen, K. S.; Jacobsen, K. W. Science, Vol. 354, Issue 6309 https://doi.org/10.1126/science.aah4776	journal	October 2016
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play Silver, David; Hubert, Thomas; Schrittwieser, Julian Science, Vol. 362, Issue 6419 https://doi.org/10.1126/science.aar6404	journal	December 2018
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
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
Design of Experiments Fisher, R. A. BMJ, Vol. 1, Issue 3923 https://doi.org/10.1136/bmj.1.3923.554-a	journal	March 1936
Nested-Batch-Mode Learning and Stochastic Optimization with An Application to Sequential MultiStage Testing in Materials Science Wang, Yingfei; Reyes, Kristofer G.; Brown, Keith A. SIAM Journal on Scientific Computing, Vol. 37, Issue 3 https://doi.org/10.1137/140971117	journal	January 2015
Optimal Learning in Experimental Design Using the Knowledge Gradient Policy with Application to Characterizing Nanoemulsion Stability Chen, Si; Reyes, Kristofer-Roy G.; Gupta, Maneesh K. SIAM/ASA Journal on Uncertainty Quantification, Vol. 3, Issue 1 https://doi.org/10.1137/140971129	journal	January 2015
Optimal Learning with Local Nonlinear Parametric Models over Continuous Designs He, Xinyu; Reyes, Kristofer G.; Powell, Warren B. SIAM Journal on Scientific Computing, Vol. 42, Issue 4 https://doi.org/10.1137/19M1245608	journal	January 2020
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
Opportunities and Challenges for Machine Learning in Materials Science Morgan, Dane; Jacobs, Ryan Annual Review of Materials Research, Vol. 50, Issue 1 https://doi.org/10.1146/annurev-matsci-070218-010015	journal	July 2020
Evolving the Materials Genome: How Machine Learning Is Fueling the Next Generation of Materials Discovery Suh, Changwon; Fare, Clyde; Warren, James A. Annual Review of Materials Research, Vol. 50, Issue 1 https://doi.org/10.1146/annurev-matsci-082019-105100	journal	July 2020
Machine Learning in Materials Discovery: Confirmed Predictions and Their Underlying Approaches Saal, James E.; Oliynyk, Anton O.; Meredig, Bryce Annual Review of Materials Research, Vol. 50, Issue 1 https://doi.org/10.1146/annurev-matsci-090319-010954	journal	July 2020
Machine Learning for Structural Materials Sparks, Taylor D.; Kauwe, Steven K.; Parry, Marcus E. Annual Review of Materials Research, Vol. 50, Issue 1 https://doi.org/10.1146/annurev-matsci-110519-094700	journal	July 2020
A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence Haenlein, Michael; Kaplan, Andreas California Management Review, Vol. 61, Issue 4 https://doi.org/10.1177/0008125619864925	journal	July 2019
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
A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy Zhu, Yanan; Ouyang, Qi; Mao, Youdong BMC Bioinformatics, Vol. 18, Issue 1 https://doi.org/10.1186/s12859-017-1757-y	journal	July 2017
A survey of transfer learning Weiss, Karl; Khoshgoftaar, Taghi M.; Wang, DingDing Journal of Big Data, Vol. 3, Issue 1 https://doi.org/10.1186/s40537-016-0043-6	journal	May 2016
Perspectives on The Rise and Fall of American Growth Gordon, Robert J. American Economic Review, Vol. 106, Issue 5 https://doi.org/10.1257/aer.p20161126	journal	May 2016
Deep learning microscopy Rivenson, Yair; Göröcs, Zoltán; Günaydin, Harun Optica, Vol. 4, Issue 11 https://doi.org/10.1364/OPTICA.4.001437	journal	January 2017
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
A data ecosystem to support machine learning in materials science Blaiszik, Ben; Ward, Logan; Schwarting, Marcus MRS Communications, Vol. 9, Issue 4 https://doi.org/10.1557/mrc.2019.118	journal	October 2019
Automating material image analysis for material discovery Park, Chiwoo; Ding, Yu MRS Communications, Vol. 9, Issue 02 https://doi.org/10.1557/mrc.2019.48	journal	April 2019
Experiment Specification, Capture and Laboratory Automation Technology (ESCALATE): a software pipeline for automated chemical experimentation and data management Pendleton, Ian M.; Cattabriga, Gary; Li, Zhi MRS Communications, Vol. 9, Issue 3 https://doi.org/10.1557/mrc.2019.72	journal	June 2019
Robocrystallographer: automated crystal structure text descriptions and analysis Ganose, Alex M.; Jain, Anubhav MRS Communications, Vol. 9, Issue 3 https://doi.org/10.1557/mrc.2019.94	journal	July 2019
Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics Vasudevan, Rama K.; Choudhary, Kamal; Mehta, Apurva MRS Communications, Vol. 9, Issue 3 https://doi.org/10.1557/mrc.2019.95	journal	July 2019
Synthetic biology—Engineering nature to make materials Ball, Philip MRS Bulletin, Vol. 43, Issue 7 https://doi.org/10.1557/mrs.2018.165	journal	July 2018
Artificial intelligence for materials discovery Gomes, Carla P.; Selman, Bart; Gregoire, John M. MRS Bulletin, Vol. 44, Issue 7 https://doi.org/10.1557/mrs.2019.158	journal	July 2019
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
Active Learning with Statistical Models Cohn, D. A.; Ghahramani, Z.; Jordan, M. I. Journal of Artificial Intelligence Research, Vol. 4 https://doi.org/10.1613/jair.295	journal	January 1996
Strategies for Multifidelity Optimization with Variable Dimensional Hierarchical Models Robinson, Theresa; Eldred, Michael; Willcox, Karen 47th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference 14th AIAA/ASME/AHS Adaptive Structures Conference 7th https://doi.org/10.2514/6.2006-1819	conference	June 2012

Similar Records

A2SD: Accelerating Scientific Innovation Through Autonomous Discovery Systems

Conference · Sat Nov 01 00:00:00 EDT 2025 · OSTI ID:3007903

Plant science decadal vision 2020–2030: Reimagining the potential of plants for a healthy and sustainable future

Journal Article · Fri Jul 31 20:00:00 EDT 2020 · Plant Direct · OSTI ID:1657259

Related Subjects

36 MATERIALS SCIENCE
additive manufacturing
algorithmic development
artificial intelligence
autonomy
carbon nanotubes
human-machine teaming
machine learning
materials discovery
research methods
workforce development

Autonomous experimentation systems for materials development: A community perspective

Citation Formats

References (116)

Similar Records

Related Subjects