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Bradford G, Lopez J, Ruza J, Stolberg MA, Osterude R, Johnson JA, Gomez-Bombarelli R, Shao-Horn Y. Chemistry-Informed Machine Learning for Polymer Electrolyte Discovery. ACS CENTRAL SCIENCE 2023; 9:206-216. [PMID: 36844492 PMCID: PMC9951296 DOI: 10.1021/acscentsci.2c01123] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Indexed: 06/18/2023]
Abstract
Solid polymer electrolytes (SPEs) have the potential to improve lithium-ion batteries by enhancing safety and enabling higher energy densities. However, SPEs suffer from significantly lower ionic conductivity than liquid and solid ceramic electrolytes, limiting their adoption in functional batteries. To facilitate more rapid discovery of high ionic conductivity SPEs, we developed a chemistry-informed machine learning model that accurately predicts ionic conductivity of SPEs. The model was trained on SPE ionic conductivity data from hundreds of experimental publications. Our chemistry-informed model encodes the Arrhenius equation, which describes temperature activated processes, into the readout layer of a state-of-the-art message passing neural network and has significantly improved accuracy over models that do not encode temperature dependence. Chemically informed readout layers are compatible with deep learning for other property prediction tasks and are especially useful where limited training data are available. Using the trained model, ionic conductivity values were predicted for several thousand candidate SPE formulations, allowing us to identify promising candidate SPEs. We also generated predictions for several different anions in poly(ethylene oxide) and poly(trimethylene carbonate), demonstrating the utility of our model in identifying descriptors for SPE ionic conductivity.
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Affiliation(s)
- Gabriel Bradford
- Department
of Mechanical Engineering, Massachusetts
Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts02139, United States
| | - Jeffrey Lopez
- Research
Laboratory of Electronics, Massachusetts
Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts02139, United States
| | - Jurgis Ruza
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts02139, United States
| | - Michael A. Stolberg
- Department
of Chemistry, Massachusetts Institute of
Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts02139, United States
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts02139, United States
| | - Richard Osterude
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts02139, United States
| | - Jeremiah A. Johnson
- Department
of Chemistry, Massachusetts Institute of
Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts02139, United States
| | - Rafael Gomez-Bombarelli
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts02139, United States
| | - Yang Shao-Horn
- Department
of Mechanical Engineering, Massachusetts
Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts02139, United States
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts02139, United States
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Mindemark J, Lacey MJ, Bowden T, Brandell D. Beyond PEO—Alternative host materials for Li + -conducting solid polymer electrolytes. Prog Polym Sci 2018. [DOI: 10.1016/j.progpolymsci.2017.12.004] [Citation(s) in RCA: 417] [Impact Index Per Article: 69.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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