An end-to-end artificial intelligence platform enables real-time assessment of superionic conductors

Abstract

Superionic conductors (SCs) exhibiting low ion migration activation energy (Ea) are critical to the performance of electrochemical energy storage devices such as solid-state batteries and fuel cells. However, it is challenging to obtain Ea experimentally and theoretically, and the artificial intelligence (AI) method is expected to bring a breakthrough in predicting Ea. Here, we proposed an AI platform (named AI-IMAE) to predict the Ea of cation and anion conductors, including Li+, Na+, Ag+, Al3+, Mg2+, Zn2+, Cu(2)+, F−, and O2−, which is ~105 times faster than traditional methods. The proposed AI-IMAE is based on crystal graph neural network models and achieves a holistic average absolute error of 0.19 eV, a median absolute error of 0.09 eV, and a Pearson coefficient of 0.92. Using AI-IMAE, we rapidly discovered 316 promising SCs as solid-state electrolytes and 129 SCs as cathode materials from 144,595 inorganic compounds. AI-IMAE is expected to completely solve the challenge of time-consuming Ea prediction and blaze a new trail for large-scale studies of SCs with excellent performance. As more experimental and high-precision theoretical data become available, AI-IMAE can train custom models and transfer the existing models to new models through transfer learning to constantly meet more demands.