Pure and Hybrid Evolutionary Computing in Global Optimization of Chemical Structures: from Atoms and Molecules to Clusters and Crystals

TL;DR

This paper reviews the recent growth of evolutionary computing methods, especially hybrid approaches with DFT, in optimizing complex chemical structures like clusters and crystals, highlighting their potential and current limitations.

Contribution

It provides a comprehensive review of pure and hybrid evolutionary computing techniques applied to chemical structure optimization, emphasizing their emerging role and future prospects.

Findings

Hybrid EC methods with DFT are increasingly effective for complex energy landscapes.

Genetic algorithms show promise in solving the Schrödinger Equation for molecules.

Limited work has been done on molecular clusters so far.

Abstract

The growth of evolutionary computing (EC) methods in the exploration of complex potential energy landscapes of atomic and molecular clusters, as well as crystals over the last decade or so is reviewed. The trend of growth indicates that pure as well as hybrid evolutionary computing techniques in conjunction of DFT has been emerging as a powerful tool, although work on molecular clusters has been rather limited so far. Some attempts to solve the atomic/molecular Schrodinger Equation (SE) directly by genetic algorithms (GA) are available in literature. At the Born-Oppenheimer level of approximation GA-density methods appear to be a viable tool which could be more extensively explored in the coming years, specially in the context of designing molecules and materials with targeted properties.