What Can Uni-Mol Do too? | Predicting the molecular properties of battery electrolytes with the integrated KPI framework
With the growing market demand for efficient and safe rechargeable batteries that can operate under extreme temperature conditions, the rapid and accurate evaluation of key properties of electrolyte molecules has become particularly important. A recent paper titled "A Knowledge–Data Dual-Driven Framework for Predicting the Molecular Properties of Rechargeable Battery Electrolytes," published in Angewandte Chemie International Edition, details an innovative approach known as the "Knowledge–Data Dual-Driven Framework" (KPI) specifically designed to predict the molecular properties of battery electrolytes, including melting point (MP), boiling point (BP), and flash point (FP). The research team skillfully combined deep learning techniques with domain-specific chemical knowledge, supported by large-scale datasets, significantly enhancing the accuracy and efficiency of predictions. In this framework, the Uni-Mol model plays a central role, demonstrating great potential in predicting the properties of electrolyte molecules and providing strong support for the development of next-generation high-performance batteries.
Xiang Chen, an associate research fellow in the Department of Chemical Engineering at Tsinghua University, is the corresponding author of the paper. Yuchen Gao, a 2022 direct-entry PhD student in the Department of Chemical Engineering, is the first author. Co-authors include Yuhang Yuan, an undergraduate from the Tsinghua Academy of Wisdom; Suozhi Huang, an undergraduate from the Institute for Interdisciplinary Information Sciences; Nan Yao, a 2020 direct-entry PhD student; Legeng Yu, a 2021 direct-entry PhD student; Yaopeng Chen, a 2023 direct-entry PhD student; and Qiang Zhang, a professor in the Department of Chemical Engineering. The research was supported by funding from the National Natural Science Foundation of China, the National Key R&D Program of China, and the Beijing Natural Science Foundation.