Autonomous continuous flow reactor synthesis for scalable atom-precision

Sumpter, Bobby G.; Hong, Kunlun; Vasudevan, Rama K.; Ivanov, Ilia; Advincula, Rigoberto

doi:10.1016/j.cartre.2022.100234

Autonomous continuous flow reactor synthesis for scalable atom-precision

Journal Article · Mon Dec 05 23:00:00 EST 2022 · Carbon Trends

DOI:https://doi.org/10.1016/j.cartre.2022.100234· OSTI ID:1905394

^[1]; ^[1]; ^[1]; ^[1]; Advincula, Rigoberto ^[2]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS); Univ. of Tennessee, Knoxville, TN (United States)

With new instrumentation design, robotics, and in-operando hyphenated analytical tool automation, the intelligent discovery of synthesis pathways is becoming feasible. It can potentially bridge the gap for the scale-up of new materials. In this article, we review current progress and describe a new system that uses an autonomous continuous flow chemistry framework to translate high-quality lead molecules and materials to quantities that can meet scalability demands. At the core is a continuous flow synthesis platform that can design its viable synthesis pathway to a particular molecule or material and then autonomously carry it out. This is realized by integrating: (1) A workflow/architecture for multimode chemical/materials characterization in-line. The in-line characterization modes are NMR, ESR, IR, Raman, UV-Vis, GC-MS, and HPLC, along with ex-situ modes for X-Ray and neutron scattering; (2) Integration for feedback/analysis/data storage of the control variables; (3) A core software stack that includes deep learning and reinforcement learning alongside quantum chemistry and molecular dynamics; (4) On-demand compute architectures that parse calculations to compute resources needed which include light-weight edge, mid-level edge (NVIDA DGX-2), and high-performance computing. We demonstrate preliminary results on how this autonomous reactor system can enhance our ability to deliver deuterated materials, copolymers, and site-substituted molecules.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Science (CNMS)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1905394

Journal Information:: Carbon Trends, Journal Name: Carbon Trends Vol. 10; ISSN 2667-0569

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (45)

Autonomous discovery in the chemical sciences part I: Progress Jensen, Klavs F.; Coley, Connor W.; Eyke, Natalie S. Angewandte Chemie International Edition https://doi.org/10.1002/anie.201909987	journal	September 2019
Microreactors as Tools for Synthetic Chemists—The Chemists' Round-Bottomed Flask of the 21st Century? Geyer, Karolin; Codée, Jeroen D. C.; Seeberger, Peter H. Chemistry - A European Journal, Vol. 12, Issue 33 https://doi.org/10.1002/chem.200600596	journal	November 2006
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
Continuous Flow Reactors, a Tool for the Modern Synthetic Chemist Wiles, Charlotte; Watts, Paul European Journal of Organic Chemistry, Vol. 2008, Issue 10 https://doi.org/10.1002/ejoc.200701041	journal	March 2008
Computer Aided Modeling of Polymer Reaction Engineering-The Status of Predici, I-Simulation Wulkow, Michael Macromolecular Reaction Engineering, Vol. 2, Issue 6 https://doi.org/10.1002/mren.200800024	journal	September 2008
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
Polymer Synthesis in Continuous Flow Reactors Zaquen, Neomy; Rubens, Maarten; Corrigan, Nathaniel Progress in Polymer Science, Vol. 107 https://doi.org/10.1016/j.progpolymsci.2020.101256	journal	August 2020
Data-Driven Strategies for Accelerated Materials Design Pollice, Robert; dos Passos Gomes, Gabriel; Aldeghi, Matteo Accounts of Chemical Research, Vol. 54, Issue 4 https://doi.org/10.1021/acs.accounts.0c00785	journal	February 2021
Real-Time Reaction Monitoring with In Operando Flow NMR and FTIR Spectroscopy: Reaction Mechanism of Benzoxazole Synthesis Chae, Yeongseok; Min, Sein; Park, Eunjoon Analytical Chemistry, Vol. 93, Issue 4 https://doi.org/10.1021/acs.analchem.0c03852	journal	January 2021
High-Throughput Routes to Biomaterials Discovery Soheilmoghaddam, Farhad; Rumble, Madeleine; Cooper-White, Justin Chemical Reviews, Vol. 121, Issue 18 https://doi.org/10.1021/acs.chemrev.0c01026	journal	July 2021
Reaction Monitoring by Benchtop NMR Spectroscopy Using a Novel Stationary Flow Reactor Setup Friebel, Anne; von Harbou, Erik; Münnemann, Kerstin Industrial & Engineering Chemistry Research, Vol. 58, Issue 39 https://doi.org/10.1021/acs.iecr.9b03048	journal	September 2019
Deuteration and Polymers: Rich History with Great Potential Li, Lengwan; Jakowski, Jacek; Do, Changwoo Macromolecules, Vol. 54, Issue 8 https://doi.org/10.1021/acs.macromol.0c02284	journal	March 2021
The Influence of Residence Time Distribution on Continuous-Flow Polymerization Reis, Marcus H.; Varner, Travis P.; Leibfarth, Frank A. Macromolecules, Vol. 52, Issue 9 https://doi.org/10.1021/acs.macromol.9b00454	journal	May 2019
Fully Automated Chemical Synthesis: Toward the Universal Synthesizer Collins, Nathan; Stout, David; Lim, Jin-Ping Organic Process Research & Development, Vol. 24, Issue 10 https://doi.org/10.1021/acs.oprd.0c00143	journal	June 2020
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
Polymerizations in Continuous Flow: Recent Advances in the Synthesis of Diverse Polymeric Materials Reis, Marcus H.; Leibfarth, Frank A.; Pitet, Louis M. ACS Macro Letters, Vol. 9, Issue 1 https://doi.org/10.1021/acsmacrolett.9b00933	journal	January 2020
The Deuterium Isotope Effect Wiberg, Kenneth B. Chemical Reviews, Vol. 55, Issue 4 https://doi.org/10.1021/cr50004a004	journal	August 1955
Atom Transfer Radical Copolymerization of Styrene and n -Butyl Acrylate Arehart, Stephen V.; Matyjaszewski, Krzysztof Macromolecules, Vol. 32, Issue 7 https://doi.org/10.1021/ma981693v	journal	April 1999
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
The past, present and potential for microfluidic reactor technology in chemical synthesis Elvira, Katherine S.; i. Solvas, Xavier Casadevall; Wootton, Robert C. R. Nature Chemistry, Vol. 5, Issue 11 https://doi.org/10.1038/nchem.1753	journal	October 2013
Big–deep–smart data in imaging for guiding materials design Kalinin, Sergei V.; Sumpter, Bobby G.; Archibald, Richard K. Nature Materials, Vol. 14, Issue 10 https://doi.org/10.1038/nmat4395	journal	September 2015
Automated extraction of chemical synthesis actions from experimental procedures Vaucher, Alain C.; Zipoli, Federico; Geluykens, Joppe Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-020-17266-6	journal	July 2020
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
Automated radial synthesis of organic molecules Chatterjee, Sourav; Guidi, Mara; Seeberger, Peter H. Nature, Vol. 579, Issue 7799 https://doi.org/10.1038/s41586-020-2083-5	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
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
Digitising chemical synthesis in automated and robotic flow Hardwick, Tomas; Ahmed, Nisar Chemical Science, Vol. 11, Issue 44 https://doi.org/10.1039/D0SC04250A	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
Photochemical reactions and on-line UV detection in microfabricated reactors Lu, Hang; Schmidt, Martin A.; Jensen, Klavs F. Lab on a Chip, Vol. 1, Issue 1 https://doi.org/10.1039/b104037p	journal	January 2001
Cross-coupling in flow Noël, Timothy; Buchwald, Stephen L. Chemical Society Reviews, Vol. 40, Issue 10 https://doi.org/10.1039/c1cs15075h	journal	January 2011
Developing Continuous-Flow Microreactors as Tools for Synthetic Chemists Seeberger, Peter; Geyer, Karolin; Gustafsson, Tomas Synlett, Vol. 2009, Issue 15 https://doi.org/10.1055/s-0029-1217828	journal	August 2009
The Scientific Response to COVID-19 and Lessons for Security Gronvall, Gigi Kwik Survival, Vol. 62, Issue 3 https://doi.org/10.1080/00396338.2020.1763613	journal	May 2020
Discovery of an -Amino C-H Arylation Reaction Using the Strategy of Accelerated Serendipity McNally, A.; Prier, C. K.; MacMillan, D. W. C. Science, Vol. 334, Issue 6059 https://doi.org/10.1126/science.1213920	journal	November 2011
Digitization of multistep organic synthesis in reactionware for on-demand pharmaceuticals Kitson, Philip J.; Marie, Guillaume; Francoia, Jean-Patrick Science, Vol. 359, Issue 6373 https://doi.org/10.1126/science.aao3466	journal	January 2018
A platform for automated nanomole-scale reaction screening and micromole-scale synthesis in flow Perera, Damith; Tucker, Joseph W.; Brahmbhatt, Shalini Science, Vol. 359, Issue 6374 https://doi.org/10.1126/science.aap9112	journal	January 2018
Predicting reaction performance in C–N cross-coupling using machine learning Ahneman, Derek T.; Estrada, Jesús G.; Lin, Shishi Science, Vol. 360, Issue 6385 https://doi.org/10.1126/science.aar5169	journal	February 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
Gradient copolymers: Synthesis, structure, and properties Zaremski, M. Yu.; Kalugin, D. I.; Golubev, V. B. Polymer Science Series A, Vol. 51, Issue 1 https://doi.org/10.1134/S0965545X09010088	journal	January 2009
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
Improving the performance of models for one-step retrosynthesis through re-ranking Lin, Min Htoo; Tu, Zhengkai; Coley, Connor W. Journal of Cheminformatics, Vol. 14, Issue 1 https://doi.org/10.1186/s13321-022-00594-8	journal	March 2022
Adaptive machine learning for efficient materials design Balachandran, Prasanna V. MRS Bulletin, Vol. 45, Issue 7 https://doi.org/10.1557/mrs.2020.163	journal	July 2020
Artificial Intelligence for Computer-Aided Synthesis In Flow: Analysis and Selection of Reaction Components Plehiers, Pieter P.; Coley, Connor W.; Gao, Hanyu Frontiers in Chemical Engineering, Vol. 2 https://doi.org/10.3389/fceng.2020.00005	journal	August 2020
NMR reaction monitoring in flow synthesis Gomez, M. Victoria; de la Hoz, Antonio Beilstein Journal of Organic Chemistry, Vol. 13 https://doi.org/10.3762/bjoc.13.31	journal	February 2017
Entropy and fragility in supercooling liquids Angell, C. A. Journal of Research of the National Institute of Standards and Technology, Vol. 102, Issue 2 https://doi.org/10.6028/jres.102.013	journal	March 1997

Similar Records

From Chaos to Clarity: Autonomous Materials Discovery for Extreme Environments

Conference · Mon Dec 09 23:00:00 EST 2024 · OSTI ID:2481438

From Chaos to Clarity: Autonomous Materials Discovery for Extreme Environments [Slides]

Technical Report · Wed Oct 30 00:00:00 EDT 2024 · OSTI ID:2481437

An autonomous laboratory for the accelerated synthesis of novel materials

Journal Article · Tue Nov 28 23:00:00 EST 2023 · Nature (London) · OSTI ID:2281696

Related Subjects

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Autonomous synthesis
Flow chemistry
Machine learning
Polymers

Autonomous continuous flow reactor synthesis for scalable atom-precision

Citation Formats

References (45)

Similar Records

Related Subjects