De novo inverse materials design by combining optimization algorithm, universal potential and universal property model

Abstract

We present a de novo inverse materials design (DNID) approach that fully automates the materials design for target physical properties, without the need to provide atomic composition, chemical stoichiometry, and crystal structure in advance. Here we used density functional theory reference data to train a universal machine learning potential (UPot), and transfer learning to train a universal bulk modulus model (UBMod). Both UPot and UBMod were able to cover materials systems composed of any elements among 42 elements. Interfaced with optimization algorithm and enhanced sampling, the DNID is applied to find the materials with the largest cohesive energy and the largest bulk modulus, respectively. NaCl-type ZrC was found to be the material with the largest cohesive energy and many other new materials were discovered to have the strong atomic cohesion, such as C, TiC, and ZrO2. For bulk modulus, diamond was identified to have the largest value and many other new carbon prototypes, several carbon borides and carbon nitrides were found to have large bulk modulus close to diamond. The DNID approach is applicable to design the materials with other multi-objective properties with accuracy limited principally by the amount, reliability and diversity of the training data. It provides a new way for the inverse materials design with other functional properties for practical applications.