Towards Universal Material Property Prediction with Deep Learning and Single-Descriptor electronic Density

Abstract

Owing to its high scalability and computational efficiency, machine learning methods have been increasingly integrated into various scientific research domains, including ab initio-based materials design. It has been demonstrated that, by incorporating modern machine learning algorithms, one can predict material properties with practically acceptable accuracy. However, one of the most significant limitations that restrict the widespread application of machine learning is its lack of transferability, as a given framework is typically applicable only to a specific property. The origin of this limitation is rooted in the fact that a material's properties are determined by multiple degrees of freedom -- and their complex interplay -- associated with nuclei and electrons, such as atomic type, structural symmetry, and the number and quantum states of the valence electrons, among others. The inherent complexity rules out the possibility of a single machine learning framework providing a full description of these critical quantities. In this paper, we develop a universal machine learning framework based solely on a physically grounded and theoretically rigorous descriptor -- electronic charge density. Our framework not only enables accurate prediction of eight different material properties (with R$^2$ values up to 0.94), but also demonstrates outstanding multi-task learning capability, as prediction accuracy improves when more target properties are incorporated into a single training process, thereby indicating excellent transferability. These results represent a significant step toward realizing the long-standing goal of a universal machine learning framework for the unified prediction of all material properties.