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Machine Learning Accelerated Genetic Algorithms for Computational Materials Search
preprintrevised on 10.12.2018, 11:15 and posted on 10.12.2018, 20:15 by Steen Lysgaard, Paul C. Jennings, Jens Strabo Hummelshøj, Thomas Bligaard, Tejs Vegge
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.