A Fast Forward Prediction Framework for Energy Materials Design Based on Machine Learning Methods

Energy materials play an important role in renewable and green energy technologies. The exploration of new materials, including nanomaterials, is important for breaking through the current bottlenecks of energy density and charging rates. However, traditional theoretical computational methods face the dilemma of long research cycles. Machine learning methods have in recent years shown considerable potential for accelerating research efforts. However, most approaches are limited to specific properties of particular devices. In this paper, we propose a forward prediction and screening framework for functional materials, which includes database selection, attributes, descriptors, machine learning models, and prediction and screening. Based on the Materials Project database, auto-encoding methods are employed to generate Coulomb matrices as the input to train the convolutional neural networks, which finally screen 12 lithium-ion, 6 zinc-ion, and 8 aluminum-ion battery cathode materials satisfying the criteria from 4,300 materials. The results show that the proposed framework can predict material performance well toward rapid initial screening. The proposed framework can provide a specific and complete working process reference for energy materials design work, contributing to the theoretical foundation for the design of core industrial software for materials engineering.