Preventing Premature Convergence in Evolutionary Structure Determination of Complex Molecular Systems: Demonstration in Few-Nanometer-Sized TiCl4-Capped MgCl2 Nanoplates

Abstract

The combination of genetic algorithm-based global search and local geometry optimization enables nonempirical structure determination for complex materials such as practical solid catalysts. However, premature convergence in the genetic algorithm hinders the determination of the global minimum for complicated molecular systems. Here, we implemented a distributed genetic algorithm based on the migration from a structure database for avoiding the premature convergence, and thus we realized the structure determination for TiCl₄-capped MgCl₂ nanoplates with experimentally consistent sizes. The obtained molecular models are featured with a realistic size and nonideal surfaces, representing actual primary particles of catalysts.