2月27日，PNAS同日上线两篇关于美国斯坦福大学崔屹教授的文章，一篇以《Profile of Yi Cui》为题介绍了崔屹教授整个科研生涯，另一篇以《Data-driven electrolyte design for lithium metal anodes》则着重介绍了崔屹教授在以往研究的基础上，通过采用新的技术-人工智能-实现电解液的筛选工作，而且提出了之前从未提出的新观点。
在此，美国斯坦福大学崔屹教授和Stacey F. Bent教授开发了机器学习（ML）模型来帮助和加速高性能电解液的设计。作者利用电解液的元素组成作为模型的特征，同时应用线性回归、随机森林和集成模型来识别预测CE的关键特征。模型显示，溶剂氧含量（sO）的降低是得到优异CE的关键。同时，使用ML模型设计了无氟溶剂的电解液配方，实现了高达99.70%的CE。这项工作突出了数据驱动方法的前景，其可以加速锂金属电池的高性能电解液的设计。
相关文章以“Data-driven electrolyte design for lithium metal anodes”为题发表在PNAS上。
Data-driven electrolyte design for lithium metal anodes
The liquid electrolyte plays a central role in lithium metal batteries, particularly in dictating the battery cyclability. Electrolyte engineering in recent years has become a promising strategy to improve cyclability in lithium metal batteries. However, owing to the complexity of electrolyte design, prediction of Coulombic efficiency and efficient design of electrolytes remains challenging. In this work, we adopt a data-driven approach to build machine learning（ML）models for electrolyte design. From our models, we extract an important yet previously unidentified insight that low solvent oxygen content can lead to superior cyclability. Leveraging this insight as a strategy, we introduce a series of electrolytes with high stability and cyclability in lithium metal batteries.
Abstract
Improving Coulombic efficiency（CE）is key to the adoption of high energy density lithium metal batteries. Liquid electrolyte engineering has emerged as a promising strategy for improving the CE of lithium metal batteries, but its complexity renders the performance prediction and design of electrolytes challenging. Here, we develop machine learning（ML）models that assist and accelerate the design of high-performance electrolytes. Using the elemental composition of electrolytes as the features of our models, we apply linear regression, random forest, and bagging models to identify the critical features for predicting CE. Our models reveal that a reduction in the solvent oxygen content is critical for superior CE. We use the ML models to design electrolyte formulations with fluorine-free solvents that achieve a high CE of 99.70%. This work highlights the promise of data-driven approaches that can accelerate the design of high-performance electrolytes for lithium metal batteries.
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