Discovery of New Anode SEI Forming Additives Using an in silico Evolutionary Approach

Abstract

To improve the performance of lithium ion batteries, functional additives are included in electrolyte formulations. To reduce damage to the electrolyte when a cell is charged, sacrificial anode SEI forming additives are used, requiring high reduction potential and high reactivity. Previously, high throughput quantum chemical screening of structure libraries has been demonstrated for electrolyte component discovery, but this can be a time consuming and highly curated procedure. An alternative approach involves the automated evolution of a set of input structures toward target property characteristics. This requires less user management and enables knowledge creation rather than knowledge implementation. In this work, a quantum chemistry based genetic optimization framework is applied to battery electrolyte components for the first time, seeking to simultaneously maximize reduction potential and minimize oxidation potential for evolved candidate anode SEI additives.