Voltage prediction of intercalation cathodes using machine learning and crystal structures

Abstract

Lithium-ion batteries have many problems as a sustainable energy resource, and the development of post-lithium-ion batteries is urgently needed. In battery design, machine learning-based screening, which has a low computational cost, has attracted much attention as an alternative to first-principles calculations. For cathode materials, the model which predicts the average voltage of intercalation cathodes from the given composition formula has been proposed. However, there are two problems. The first one is that the composition formula is incompatible with the detailed property evaluation by first-principles calculations. The second one is that it is hard to evaluate the applicability domain of the model. In this work, we developed the model which predicts the average voltage from crystal structures and evaluates the reliability of the prediction easily. Compared with the previous model, our model overcame the problems of the previous model while maintaining accuracy. We will perform the machine learning-based high throughput screening for sodium-ion battery cathodes by using our model.