Machine-learning accelerated annealing with fitting-search style for multicomponent alloy structure predictions

Abstract

Structural prediction for the discovery of novel materials is a long sought-after goal of computational physics and materials sciences. The success is rather limited for methods such as the simulated annealing method (SA) that require expensive density functional theory (DFT) calculations and follow unintelligent search paths. Here a machine-learning based crystal combinatorial optimization program (CCOP) with a fitting-search style is proposed to drastically improve the efficiency of structural search in SA. CCOP uses a graph neural network energy prediction model to reduce the DFT cost and a deep reinforcement learning algorithm to direct the search path. Tests on six multicomponent alloys show the energy prediction model is capable of extracting the bonding characteristics of the complex alloys to achieve interpretability. It also achieves high accuracy with a tiny training set (an increment of 30 samples per iteration) by active learning in less than five iterations. Comparison with a few conventional methods shows that CCOP finds the lowest-energy structures with the smallest number of search steps. CCOP cuts the computing cost of SA by two orders of magnitude, while providing better search results than SA. CCOP is promising for serving as a broadly applicable tool for the efficient crystal structure predictions.