Combining Evolutionary Algorithms with Clustering toward Rational Global Structure Optimization at the Atomic Scale.

Abstract

Predicting structures at the atomic scale is of great importance for understanding the properties of materials. Such predictions are infeasible without efficient global optimization techniques. Many current techniques produce a large amount of idle intermediate data before converging to the global minimum. If this information could be analyzed during optimization, many new possibilities emerge for more rational search algorithms. We combine an evolutionary algorithm (EA) and clustering, a machine-learning technique, to produce a rational algorithm for global structure optimization. Clustering the configuration space of intermediate structures into regions of geometrically similar structures enables the EA to suppress certain regions and favor others. For two test systems, an organic molecule and an oxide surface, the global minimum search proves significantly faster when favoring stable structures in unexplored regions. This clustering-enhanced EA is a step toward adaptive global optimization techniques that can act upon information in accumulated data.