Neural-network-backed evolutionary search for SrTiO₃(110) surface reconstructions.

ABSTRACT: The determination of atomic structures in surface reconstructions has typically relied on structural models derived from intuition and domain knowledge. Evolutionary algorithms have emerged as powerful tools for such structure searches. However, when density functional theory is used to evaluate the energy the computational cost of a thorough exploration of the potential energy landscape is prohibitive. Here, we drive the exploration of the rich phase diagram of TiO _x overlayer structures on SrTiO₃(110) by combining the covariance matrix adaptation evolution strategy (CMA-ES) and a neural-network force field (NNFF) as a surrogate energy model. By training solely on SrTiO₃(110) 4×1 overlayer structures and performing CMA-ES runs on 3×1, 4×1 and 5×1 ov

SUBMITTER: Wanzenbock R 

PROVIDER: S-EPMC9549766 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature