Prediction of the low-temperature properties of electrolyte solvents for lithium-ion batteries via machine learning

Nanoscale 2026 Open access

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Authors: Jiechen Guo, Yifan Chai, Cancan Hong, Hongtai Liu, Lijing Xie, Tianle Wang, Jingpeng Chen, Ge Song, Zonglin Yi, Fangyuan Su
DOI: 10.1039/d5nr03942h
Cited by: 4
References: 56

Abstract

using an artificial neural network. To further uncover structure-property relationships, t-SNE and SHAP are employed to analyze the molecular features contributing to thermal behavior at a microscopic level. Finally, by integrating molecular neighborhood search with high-throughput screening, nine candidate molecules are identified as promising low-temperature electrolytes for LIBs. This work provides an efficient and generalizable framework for the design of low-temperature electrolytes in LIBs.

Fields of Study & Subjects

Machine Learning in Materials Science Advanced Battery Materials and Technologies Advanced Battery Technologies Research

Publication details

Journal / source
Nanoscale
Publisher
Royal Society of Chemistry (RSC)
Volume
18
Issue
5
Pages
2613–2624
Publication date
1 January 2026
Language
EN