  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
multi-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 5 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.
