Genetic algorithms for computational materials discovery accelerated by machine learning

Abstract

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

Abstract of "Genetic algorithms for computational materials discovery accelerated by machine learning" by Paul C. Jennings, Steen Lysgaard, Jens S. Hummelshøj et al., https://doi.org/10.1038/s41524-019-0181-4, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.