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Machine Learning Accelerated Genetic Algorithms for Computational Materials Search
Energy & Materials
Machine Learning Accelerated Genetic Algorithms for Computational Materials Search 1 Minute Read

TRI Author: Jens Hummelshøj

All Authors: 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. Read More

Citation: Lysgaard, Steen, Paul C. Jennings, Jens Strabo Hummelshøj, Thomas Bligaard, and Tejs Vegge. "Machine Learning Accelerated Genetic Algorithms for Computational Materials Search." In ChemRxiv(2018).