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.

Gabriel Bradford

Jeffrey Lopez

Jurgis Ruza

Michael A. Stolberg

Richard Osterude

Jeremiah A. Johnson

Rafael Gomez-Bombarelli

Yang Shao-Horn

ACS Central Science

ACS CENTRAL SCIENCE 发表关于化学和相关领域研究的最引人注目、最重要的主要报告，其中化学方法发挥着关键作用。它也是美国化学学会出版的第一个完全开放获取的期刊。

ACS Central Science publishes the most significant and noteworthy primary reports on research in chemistry and related fields, particularly those where chemical methods play a crucial role. It is also the first fully open access journal published by the American Chemical Society.

《ACS Central Science》发表关于化学及相关领域的最重要和最引人注目的研究报告，特别是那些化学方法发挥关键作用的研究。这是美国化学学会出版的首个完全开放获取的期刊。

Category	Quartile
化学	1
化学, 化学综合	1

Chemistry-Informed Machine Learning for Polymer Electrolyte Discovery

ivySCI AI Smartly Parses PDF, Answers Researchers' Questions, and Helps You Understand Papers in Seconds

Journal Info

Category	Quartile
CHEMISTRY, MULTIDISCIPLINARY	1