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Discovery of Innovative Polymers for Next-Generation Gas-Separation Membranes using Interpretable Machine Learning

Yang, Jason and Tao, Lei and He, Jinlong and McCutcheon, Jeffrey R. and Li, Ying (2021) Discovery of Innovative Polymers for Next-Generation Gas-Separation Membranes using Interpretable Machine Learning. . (Unpublished)

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Polymer membranes perform innumerable separations with far-reaching environmental implications. Despite decades of research on membrane technologies, 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 separation performance. Specifically, multitask ML models are trained on available experimental data to link polymer chemistry to gas permeabilities of He, H₂, O₂, N₂, CO₂, and CH₄. We interpret the ML models and extract chemical heuristics for membrane design, through Shapley Additive exPlanations (SHAP) analysis. We then screen over nine million hypothetical polymers through our models and identify thousands of candidates that lie well above current performance upper bounds. Notably, we discover hundreds of never-before-seen ultrapermeable polymer membranes with O₂ and CO₂ permeability greater than 10⁴ and 10⁵ Barrer, respectively, orders of magnitude higher than currently available polymeric membranes. These hypothetical polymers are capable of overcoming undesirable trade-off relationship between permeability and selectivity, thus significantly expanding the currently limited library of polymer membranes for highly efficient gas separations. 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:Report or Paper (Discussion Paper)
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URLURL TypeDescription Paper
Yang, Jason0000-0003-3184-1550
Additional Information:The content is available under CC BY 4.0 License. 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: Dr. Ming-Jen 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. 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. The authors also 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. We would also like to thank Dr. Mayur Ostwal for helpful comments on the manuscript. 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 anuscript, and all authors contributed to revising the manuscript. The authors declare no competing interests. The author(s) have declared ethics committee/IRB approval is not relevant to this content. The supporting information is available free of charge.
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
Subject Keywords:machine learning; polymer informatics; gas separation; molecular dynamics
Record Number:CaltechAUTHORS:20220104-157940000
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Official Citation:Yang, J., Tao, L., He, J., McCutcheon, J., & Li, Y. (2021). Discovery of Innovative Polymers for Next-Generation Gas-Separation Membranes using Interpretable Machine Learning. ChemRxiv. doi:10.26434/chemrxiv-2021-p4g7z
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:112686
Deposited By: George Porter
Deposited On:04 Jan 2022 22:11
Last Modified:04 Jan 2022 22:11

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