Predicting Reactivity and Passivation of Solid-State Battery Interfaces.

ABSTRACT: In this work, we build a computationally inexpensive, data-driven model that utilizes atomistic structure information to predict the reactivity of interfaces between any candidate solid-state electrolyte material and a Li metal anode. This model is trained on data from ab initio molecular dynamics (AIMD) simulations of the time evolution of the solid electrolyte-Li metal interfaces for 67 different materials. Predicting the reactivity of solid-state interfaces with ab initio techniques remains an elusive challenge in materials discovery and informatics, and previous work on predicting interfacial compatibility of solid-state Li-ion electrolytes and Li metal anodes has focused mainly on thermodynamic convex hull calculations. Our framework involves training machine learning mo

SUBMITTER: Lomeli EG 

PROVIDER: S-EPMC11441401 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature