Accurately predicting ionic conductivity is key to accelerating the discovery of solid-state electrolytes; yet, high-throughput experiments are labor-intensive, and first-principles simulations remain too slow. Previously, machine-learning models have been developed that incorporate structural, potential, or compositional information for prediction. Still, their poor prediction accuracy for the distinct ionic conductivities of polymorphic materials with identical compositions but different crystal structures remains problematic. Here, we present a three-dimensional convolutional neural network that learns from both the three-dimensional geometry and the spatial potential landscape of lithium ions. The former is expressed by the lithium-ion distribution, and the latter is encoded from the Bond Valence Sum Energy to predict ionic conductivity. The proposed model, trained on experimental ionic conductivities, achieves prediction performance comparable to that of a state-of-the-art model that uses composition information, and it ranks the mobilities of several materials sharing the same composition but different structures. Our findings highlight the effectiveness of incorporating structural information into conductivity prediction models to overcome the limitations of conventional approaches, potentially improving the screening of solid electrolytes by utilizing the three-dimensional physical descriptor.
Hashizume et al. (Thu,) studied this question.