Stable crystal structure prediction using machine learning-based formation energy and empirical potential function

Abstract

Crystal structure prediction aims to predict stable and easily experimentally synthesized materials, which accelerates the discovery of new materials. It is worth noting that the stability of materials is the basis for ensuring high performance and reliable application of materials. Among which, the thermodynamic and molecular dynamics stability is especially important. Therefore, this paper proposes a method to predict stable crystal structures using formation energy and Lennard-Jones potential as evaluation indicators. Specifically, we use graph neural network models to predict the formation energy of crystals, and employ empirical formulas to calculate the Lennard-Jones potential. Then, we apply Bayesian optimization algorithms to search for crystal structures with low formation energy and Lennard-Jones potential approaching zero, in order to ensure the thermodynamic stability and dynamics stability of materials. In addition, considering the impact of the bonding situation between atoms in the crystal on the structural stability, this article uses contact map to analyze the atomic bonding situation of each crystal to screen out more stable materials. Finally, the experimental results show that the method we proposed can not only reduce the time for crystal structure prediction, but also ensure the stability of crystal materials.