Enhancing Crystal Structure Prediction by decomposition methods based on graph theory

TL;DR

This paper introduces graph theory-based decomposition methods to improve the efficiency and success rate of crystal structure prediction algorithms, especially for larger systems, by reducing the search space.

Contribution

It proposes novel crossover-mutation schemes using quotient graphs to decompose periodic networks, enhancing evolutionary search performance.

Findings

Significant improvement in success rate and efficiency over standard methods.

Effective in both isolated molecules and extended crystal systems.

Validated on high-pressure phases of various materials.

Abstract

Crystal structure prediction algorithms have become powerful tools for materials discovery in recent years, however, they are usually limited to relatively small systems. The main challenge is that the number of local minima grows exponentially with system size. In this work, we proposed two crossover-mutation schemes based on graph theory to accelerate the evolutionary structure searching. These schemes can detect molecules or clusters inside periodic networks using quotient graphs for crystals and the decomposition can dramatically reduce the searching space. Sufficient examples for the test, including the high pressure phases of methane, ammonia, MgAl2O4, and boron, show that these new evolution schemes can obviously improve the success rate and searching efficiency compared with the standard method in both isolated and extended systems.