Chemistry-Informed Machine Learning for Polymer Electrolyte Discovery
Name
bradford-et-al-2023-chemistry-informed-machine-learning-for-polymer-electrolyte-discovery.pdf
Description
Published version
Size
3.55 MB
Format
Adobe PDF
Checksum (MD5)
f6127241929cfd4f8cb27ae2473b9fe4
Author(s)
Bradford, Gabriel
Lopez, Jeffrey
Ruza, Jurgis
Stolberg, Michael A.
Osterude, Richard
Johnson, Jeremiah A.
Gomez-Bombarelli, Rafael
Shao-Horn, Yang
Date Issued
January 23, 2023
Journal
ACS Central Science
Publisher
American Chemical Society
Citation
Gabriel Bradford, Jeffrey Lopez, Jurgis Ruza, Michael A. Stolberg, Richard Osterude, Jeremiah A. Johnson, Rafael Gomez-Bombarelli, and Yang Shao-Horn ACS Central Science 2023 9 (2), 206-216.
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Research Laboratory of Electronics
Massachusetts Institute of Technology. Department of Chemistry
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1021/acscentsci.2c01123
