Frontispiz: A Knowledge–Data Dual-Driven Framework for Predicting the Molecular Properties of Rechargeable Battery Electrolytes

Yu-Chen Gao, Yu-Hang Yuan, Suozhi Huang, Nan Yao, Legeng Yu, Yao-Peng Chen, Qiang Zhang, Xiang Chen
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Abstract

In their Research Article (e202416506), Xiang Chen and co-authors developed a knowledge–data dual-driven framework that incorporates domain expertise into artificial intelligence models, achieving notable accuracy in predicting properties such as melting, boiling, and flash points of battery electrolytes.

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预测可充电电池电解质分子特性的知识-数据双驱动框架
在他们的研究文章(e202416506)中,Xiang Chen及其合作者开发了一个知识数据双驱动框架,该框架将领域专业知识整合到人工智能模型中,在预测电池电解质的熔化、沸腾和闪点等特性方面取得了显著的准确性。
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来源期刊
Angewandte Chemie
Angewandte Chemie 化学科学, 有机化学, 有机合成
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