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Machine learning enables interpretable discovery of innovative polymers for gas separation membranes

Yang, Jason and Tao, Lei and He, Jinlong and McCutcheon, Jeffrey R. and Li, Ying (2022) Machine learning enables interpretable discovery of innovative polymers for gas separation membranes. Science Advances, 8 (29). Art. No. eabn9545. ISSN 2375-2548. doi:10.1126/sciadv.abn9545.

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Polymer membranes perform innumerable separations with far-reaching environmental implications. Despite decades of research, design of new membrane materials remains a largely Edisonian process. To address this shortcoming, we demonstrate a generalizable, accurate machine learning (ML) implementation for the discovery of innovative polymers with ideal performance. Specifically, multitask ML models are trained on experimental data to link polymer chemistry to gas permeabilities of He, H₂, O₂, N₂, CO₂, and CH₄. We interpret the ML models and extract valuable insights into the contributions of different chemical moieties to permeability and selectivity. We then screen over 9 million hypothetical polymers and identify thousands that lie well above current performance upper bounds, including hundreds of never-before-seen ultrapermeable polymer membranes with O₂ and CO₂ permeability greater than 10⁴ and 10⁵ Barrers, respectively. High-fidelity molecular dynamics simulations confirm the ML-predicted gas permeabilities of the promising candidates, which suggests that many can be translated to reality.

Item Type:Article
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URLURL TypeDescription
Yang, Jason0000-0003-3184-1550
Tao, Lei0000-0002-8285-1356
McCutcheon, Jeffrey R.0000-0002-5638-4926
Li, Ying0000-0002-1487-3350
Additional Information:© 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). Received: 4 January 2022. Accepted: 7 June 2022. We acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin (Frontera project and National Science Foundation Award 1818253) and National Renewable Energy Laboratory (Eagle Computing System) for providing HPC resources that have contributed to the research results reported within this paper. J.Y. would like to thank S. Krishnaswamy, M. Amodio, and A. Haji-Akbari for their guidance on efforts related to the project. We would like to thank M. Ostwal for helpful comments on the manuscript. We gratefully acknowledge financial support from the Air Force Office of Scientific Research through the Air Force’s Young Investigator Research Program (FA9550-20-1-0183; program manager: M.-J. Pan) and the National Science Foundation (CMMI-1934829 and CAREER Award CMMI-2046751). Y.L. would like to express thanks for the support from 3M’s Non-Tenured Faculty Award. Y.L. and J.R.M. would like to thank the support from the National Alliance for Water Innovation (NAWI), funded by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy (EERE), Advanced Manufacturing Office, under Funding Opportunity Announcement Number DE-FOA-0001905. J.Y. was supported by the National Science Foundation Graduate Research Fellowship under fellow ID 2021309491. This research also benefited in part from the computational resources and staff contributions provided by the Booth Engineering Center for Advanced Technology (BECAT) at UConn. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the U.S. Department of Defense. Author contributions: Y.L. and L.T. conceived the idea and supervised the research. J.Y. and L.T. collected and analyzed the data and implemented the ML models. J.H. and Y.L. developed and analyzed the molecular simulations. J.Y., L.T., J.R.M., and Y.L. contributed to the design of the project and data analysis. J.Y., L.T., and J.H. wrote the first draft of the manuscript, and all authors contributed to revising the manuscript. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. The datasets used in this work can be accessed from The authors declare that they have no competing interests.
Funding AgencyGrant Number
Air Force Office of Scientific Research (AFOSR)FA9550-20-1-0183
National Alliance for Water InnovationUNSPECIFIED
Department of Energy (DOE)DE-FOA-0001905
NSF Graduate Research Fellowship2021309491
Issue or Number:29
Record Number:CaltechAUTHORS:20220722-768512000
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Official Citation:Yang J, Tao L, He J, McCutcheon JR, Li Y. Machine learning enables interpretable discovery of innovative polymers for gas separation membranes. Sci Adv. 2022;8(29):eabn9545. doi:10.1126/sciadv.abn9545
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:115754
Deposited By: George Porter
Deposited On:22 Jul 2022 16:50
Last Modified:22 Jul 2022 16:50

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