Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305.
Department of Chemical Engineering, Stanford University, Stanford, CA 94305.
Proc Natl Acad Sci U S A. 2023 Mar 7;120(10):e2214357120. doi: 10.1073/pnas.2214357120. Epub 2023 Feb 27.
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.
提高库仑效率（CE）是推广高能量密度锂金属电池的关键。液体电解质工程已成为提高锂金属电池 CE 的一种很有前途的策略，但它的复杂性使得电解质的性能预测和设计具有挑战性。在这里，我们开发了机器学习（ML）模型，以协助和加速高性能电解质的设计。我们使用电解质的元素组成作为模型的特征，应用线性回归、随机森林和袋装模型来确定预测 CE 的关键特征。我们的模型表明，减少溶剂中的氧含量对于获得优异的 CE 至关重要。我们使用 ML 模型来设计无氟溶剂的电解质配方，实现了高达 99.70%的高 CE。这项工作强调了数据驱动方法的前景，它可以加速锂金属电池高性能电解质的设计。
