Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

03 December 2018, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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.


Genetic algorithms
Nanoparticle Catalysts
Machine Learning
Gaussian process regression model
Icosahedral Pt Au nanoparticles
Density Functional Theory
Effective Medium Theory


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