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Chemistry-Informed Machine Learning for Polymer Electrolyte Discovery.

Gabriel Bradford¹, Jeffrey Lopez², Jurgis Ruza³

¹Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts02139, United States.

ACS Central Science

|February 27, 2023

Summary

A new machine learning model accelerates the discovery of solid polymer electrolytes (SPEs) for safer, high-energy lithium-ion batteries. This chemistry-informed approach accurately predicts ionic conductivity, overcoming a key limitation of SPEs.

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Area of Science:

Materials Science
Electrochemistry
Computational Chemistry

Background:

Solid polymer electrolytes (SPEs) offer enhanced safety and higher energy density for lithium-ion batteries.
SPEs currently exhibit lower ionic conductivity compared to liquid and solid ceramic electrolytes, hindering their practical application.
Accelerating the discovery of SPEs with high ionic conductivity is crucial for advancing battery technology.

Purpose of the Study:

To develop a chemistry-informed machine learning model for accurate prediction of SPE ionic conductivity.
To overcome the limitations of existing models by incorporating temperature dependence.
To identify promising SPE formulations for next-generation batteries.

Main Methods:

Developed a machine learning model integrating the Arrhenius equation into a message passing neural network.
Trained the model on ionic conductivity data from hundreds of experimental publications.
Predicted ionic conductivity for thousands of candidate SPE formulations.

Main Results:

The chemistry-informed model significantly improved prediction accuracy by encoding temperature dependence.
Identified promising candidate SPE formulations with high ionic conductivity.
Demonstrated the model's utility in predicting conductivity for various anions and polymer hosts.

Conclusions:

Chemistry-informed machine learning models can effectively accelerate the discovery of advanced materials like SPEs.
Incorporating physical principles, such as temperature dependence, enhances model accuracy, especially with limited data.
This approach facilitates the identification of novel SPEs for safer and more powerful lithium-ion batteries.