Phonon DOS-Based Machine Learning Model for Designing High-Performance Solid Electrolytes in Li-Ion Batteries

Abstract

In the present work, a machine learning (ML) model was built to design solid electrolytes with improved ionic conductivity for Li-ion batteries (LIBs), and the model was based on the phonon density of states (PhDOS). Compounds with PhDOS calculations were collected from the Materials Project (MP) and processed to obtain frequencies and PhDOS to calculate the total phonon band center, a proxy for ionic conductivity. Total phonon band centers were involved in a learning process using four ML algorithms (extra random trees (XT), gradient boosting (GB), extreme gradient boosting (XGB), and decision trees (DT)). The cross-validation results from the algorithms showed that the performance of the XT-model was superior and confirmed through density functional theory- (DFT-) based phonon calculations conducted on LiYO3, Li4CO4, LiNiO3, LiGeO3, and LiSiO3. The XT-model was then used to predict the total phonon band centers of new compounds where these had no phonon calculations beforehand. Experimental validation of the XT-model involved electrochemical impedance spectroscopy (EIS) measurements on two compounds: Li2CO3 from the high range and Li6PS5Cl from the low range. Additionally, LiBiO2 that is predicted to have low total phonon band center, according to the XT-model, was considered further to estimate its potential as solid electrolyte in LIBs.