Data-driven electrolyte design for lithium metal anodes

被引：101

作者：

Kim, Sang Cheol ^{[1
]}

Oyakhire, Solomon T. ^{[2
]}

Athanitis, Constantine ^{[1
]}

Wang, Jingyang ^{[1
]}

Zhang, Zewen ^{[1
]}

Zhang, Wenbo ^{[1
]}

Boyle, David T. ^{[3
]}

Kim, Mun Sek ^{[1
,2
]}

Yu, Zhiao ^{[3
]}

Gao, Xin ^{[1
]}

Sogade, Tomi ^{[1
]}

Wu, Esther ^{[1
]}

Qin, Jian ^{[2
]}

Bao, Zhenan ^{[2
]}

Bent, Stacey F. ^{[2
,4
]}

Cui, Yi ^{[1
,4
,5
]}

机构：

[1] Stanford Univ, Dept Mat Sci & Engn, Stanford, CA 94305 USA

[2] Stanford Univ, Dept Chem Engn, Stanford, CA 94305 USA

[3] Stanford Univ, Dept Chem, Stanford, CA 94305 USA

[4] Stanford Univ, Dept Energy Sci & Engn, Stanford, CA 94305 USA

[5] Stanford Inst Mat & Energy Sci, Stanford Linear Accelerator Ctr Natl Accelerator L, Menlo Pk, CA 94025 USA

来源：

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA | 2023年 / 120卷 / 10期

关键词：

battery; energy storage; machine learning; electrolyte; COULOMBIC EFFICIENCY; HEALTH ESTIMATION; BATTERIES; ENERGY; INTERPHASES; PERFORMANCE; STATE;

D O I：

10.1073/pnas.2214357120

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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-perfor-mance 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 electro-lyte 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.

引用

页数：8

共 56 条

[1] Accurate Determination of Coulombic Efficiency for Lithium Metal Anodes and Lithium Metal Batteries [J].