Better utilization of materials’ compositions for predicting their properties: Material composition visualization network

Abstract

Owing to the complexity and diversity of advanced high-performance materials, it is challenging to comprehensively understand a material’s composition–process–structure–performance relationship. Data-driven approaches have been regarded as the fourth paradigm of new materials R&D. However, the complexity of constituent elements in many material datasets leads to very sparse compositional features, posing a tremendous challenge to machine learning models. In this study, a data mapping scheme based on fundamental atomic features was used to visualize the chemical composition characteristics mapped into two-dimensional grayscale image data to solve the problem of sparse material composition matrix. Based on this, a material composition visualization network (MCVN) is proposed and applied to predict the mechanical properties of steel and classify amorphous alloy materials. We compared the MCVN to other machine learning methods. The MCVN had an average R2 value improvement of 4% on the four targets in the National Institute for Materials Science’s (NIMS’s) steel dataset, where other models already get an average R2 of 0.92, and it achieved an R2 of 0.835 on the cross-sectional shrinkage target in the Shanghai Research Institute of Materials’(SRIM’s) steel dataset where the other models only had an average R2 of 0.64. For the unbalanced amorphous alloy material dataset, the MCVN improved the average Recall of the small-class crystalline alloy (CRA) from 0.58 to 0.78. The method based on expanding the material chemical composition information is universal and provides a new paradigm for material property prediction.