Predicting Reactivity and Passivation of Solid-State Battery Interfaces

22 April 2024, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


In this work, we build a computationally inexpensive, data-driven model that utilizes only 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 interface 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 models on AIMD data, thereby capturing information on both kinetics and thermodynamics, and then leveraging these models to predict the reactivity of thousands of new candidates in the span of seconds, avoiding the need for additional weeks-long AIMD simulations. We identify over 300 new chemically stable and over 780 passivating solid-electrolytes that are predicted to be thermodynamically unfavored. Our results indicate many potential solid-state electrolyte candidates have been incorrectly labeled unstable via purely thermodynamic approaches using density functional theory (DFT) energetics, and that the pool of promising, Li-stable solid-state electrolyte materials may be much larger than previously thought from screening efforts. To showcase the value of our approach, we highlight two borate materials that were identified by our model and confirmed by further AIMD calculations to likely be highly conductive and chemically stable with Li: LiB13C2 and LiB12PC.


Solid State Batteries
Solid State Electrolytes

Supplementary materials

Supplementary Information
1) Solid State Electrolyte (SSE) Features Considered for Building Linear Models 2) AIMD Snapshots for LiDy2 and Li6ZnGe2O8. 3) Training Set in Feature Space. 4) Stable Model Linear Cofficients. 5) Passivating Model Linear Coefficients 6) Histograms for linear model predictions.


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