Chemistry-Informed Machine Learning for Polymer Electrolyte Discovery

Gabriel Bradford, Jeffrey Lopez, Jurgis Ruza, Michael A. Stolberg, Richard Osterude, Jeremiah A. Johnson, Rafael Gomez-Bombarelli*, Yang Shao-Horn*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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.

Original languageEnglish (US)
JournalACS Central Science
StateAccepted/In press - 2022

ASJC Scopus subject areas

  • General Chemical Engineering
  • General Chemistry


Dive into the research topics of 'Chemistry-Informed Machine Learning for Polymer Electrolyte Discovery'. Together they form a unique fingerprint.

Cite this