Machine learning prediction of activation energy in cubic Li-argyrodites with hierarchically encoding crystal structure-based (HECS) descriptors.

Rational design of solid-state electrolytes (SSEs) with high ionic conductivity and low activation energy (E) is vital for all solid-state batteries. Machine learning (ML) techniques have recently been successful in predicting Li conduction property in SSEs with various descriptors and accelerating the development of SSEs. In this work, we extend the previous efforts and introduce a framework of ML prediction for E in SSEs with hierarchically encoding crystal structure-based (HECS) descriptors. Taking cubic Li-argyrodites as an example, an E prediction model is developed to the coefficient of determination (R) and root-mean-square error (RMSE) values of 0.887 and 0.02 eV for training dataset, and 0.820 and 0.02 eV for test dataset, respectively by partial least squares (PLS) analysis, proving the prediction power of HECS-descriptors. The variable importance in projection (VIP) scores demonstrate the combined effects of the global and local Li conduction environments, especially the anion size and the resultant structural changes associated with anion site disorder. The developed E prediction model directs us to optimize and design new Li-argyrodites with lower E, such as LiPSCl (<0.322 eV), LiPSBr (<0.273 eV), LiPSBrI (<0.352 eV), LiPNSI (<0.420 eV), LiAsNSI (<0.371 eV), LiAsNSeI (<0.450 eV), by broadening bottleneck size, invoking site disorder and activating concerted Li conduction. This analysis shows great potential in promoting rational design of advanced SSEs and the same approach can be applied to other types of materials.