Machine learning assisted prediction in the discharge capacities of novel MXene cathodes for aluminum ion batteries

Abstract

Conventional experimental methods are widely utilized to discover novel cathode materials for aluminum ion batteries (AIB), but their high price and low-efficiency present extra challenges when dealing with the vast family of MXene materials. In order to vigorously promote the application and development of MXene cathodes in aluminum ion batteries, a new accelerable method to predict the discharge capacities based on machine learning is proposed. Firstly, we establish and train the machine learning models which extract from experimental results of several MXene cathodes (Nb2C, Ti3C2, and V2C) to predict the battery status and initial discharge capacity of mentioned cathodes. Secondly, we refine the models using few Ti2C experimental results to successfully predict the battery status and initial discharge capacity for new Ti2C cathode, which exhibit higher capacity and longer cycle life than Nb2C, Ti3C2 and V2C cathodes. This work provides a new method for predicting electrochemical properties of advanced energy materials beyond MXene materials for aluminum-ion batteries, including but not limited to carbon materials or transition metal oxides.