10.26434/chemrxiv.7411172.v1
Steen Lysgaard
Paul C. Jennings
Jens Strabo HummelshÃ¸j
Thomas Bligaard
Tejs Vegge
Machine Learning Accelerated Genetic Algorithms for Computational Materials Search
2018
ChemRxiv
Genetic algorithms
Nanoparticle Catalysts
Machine Learning
Gaussian process regression model
PtAu
Icosahedral Pt Au nanoparticles
Density Functional Theory
GPAW
Effective Medium Theory
2018-12-03 17:20:54
article
https://chemrxiv.org/articles/Machine_Learning_Accelerated_Genetic_Algorithms_for_Computational_Materials_Search/7411172
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.