Discovery of Effective Halide Solid Electrolytes for Solid-State Rechargeable Batteries via Machine Learning and DFT Calculations

Discovery of Effective Halide Solid Electrolytes for Solid-State Rechargeable Batteries via Machine Learning and DFT Calculations

ACS Applied Energy Materials

10.1021/acsaem.5c03277

https://doi.org/10.1021/acsaem.5c03277

Li Yan Anthony Choong, Zhong Chen, Man-Fai Ng

Machine Learning, DFT, solid state electrolytes, halide

16 December 2025

Choong, L. Y. A., Chen, Z., & Ng, M.-F. (2025). Discovery of Effective Halide Solid Electrolytes for Solid-State Rechargeable Batteries via Machine Learning and DFT Calculations. ACS Applied Energy Materials. https://doi.org/10.1021/acsaem.5c03277

Halide solid electrolytes (SEs) are a strong candidate for next-generation lithium based solid-state batteries for their potential to possess a balance of key properties including ionic conductivity, mechanical properties, and electrochemical stability window (ESW) and can be synthesized using environmentally friendly processes. However, there is a lack of halides simultaneously fulfilling all the mentioned key properties, and searching for the right candidates via experiments is proven challenging. In this work, we develop a computational approach combining machine learning (ML) and DFT calculations, to discover promising novel halide SEs that satisfy several bulk properties via multi-property predictions. Various ML and deep learning (DL) models are compared to predict ionic conductivity, bulk and shear moduli, and ESW. The CatBoost, Light Gradient Boosting (LGBM), and Skorch Neural Network (NN) models are found to yield high prediction accuracies for the mentioned properties, with minimum average classification accuracies and average R2 scores exceeding 80% and 0.70 respectively. DFT verifications are performed on Rb2LiBiCl6, LiHF2, and Rb2LiAlF6, with the results suggesting Rb2LiAlF6 as a promising candidate for high voltage battery applications. Overall, we demonstrate that the current ML + DFT approach is useful in screening potential halide solid-state electrolytes that can satisfy several key SE properties.

Publisher Copyright

This research / project is supported by the A*STAR - Manufacturing, Trade, and Connectivity Programmatic funding
Grant Reference no. : M23L9b0052

This research / project is supported by the National Research Foundation Singapore - 2023 Singapore-China Joint Flagship Project (Clean Energy)
Grant Reference no. : N.A.

This document is the Accepted Manuscript version of a Published Work that appeared in final form in ACS Applied Energy Materials, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acsaem.5c03277.

https://oar.a-star.edu.sg/communities-collections/articles/22642

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