GAMaterial—A genetic‐algorithm software for material design and discovery

Genetic algorithms (GAs) are stochastic global search methods inspired by biological evolution. They have been used extensively in chemistry and materials science coupled with theoretical methods, ranging from force‐fields to high‐throughput first‐principles methods. The methodology allows an accurate and automated structural determination for molecules, atomic clusters, nanoparticles, and solid surfaces, fundamental to understanding chemical processes in catalysis and environmental sciences, for instance. In this work, we propose a new genetic algorithm software, GAMaterial, implemented in Python3.x, that performs global searches to elucidate the structures of atomic clusters, doped clusters or materials and atomic clusters on surfaces. For all these applications, it is possible to accelerate the GA search by using machine learning (ML), the ML@GA method, to build subsequent populations. Results for ML@GA applied for the dopant distributions in atomic clusters are presented. The GAMaterial software was applied for the automatic structural search for the Ti6O12 cluster, doping Al in Si11 (4Al@Si11) and Na10 supported on graphene (Na10@graphene), where DFTB calculations were used to sample the complex search surfaces with reasonably low computational cost. Finally, the global search by GA of the Mo8C4 cluster was considered, where DFT calculations were made with the deMon2k code, which is interfaced with GAMaterial. The GAMaterial software, implemented in Python3.x, performs global searches using genetic algorithms to elucidate or determine the structures of atomic clusters, defects (doping or vacancy) in clusters or solids, atomic clusters on surfaces and surface reconstruction. Machine learning methods are available to accelerate the search.