Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

Steen Lysgaard; Paul C. Jennings; Jens Strabo Hummelshøj; Thomas Bligaard; Tejs Vegge

doi:10.26434/chemrxiv.7411172.v2

Theoretical and Computational Chemistry

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Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

10 December 2018, Version 2

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This content is a preprint and has not undergone peer review at the time of posting.

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.

Keywords

Genetic algorithms

Nanoparticle Catalysts

Machine Learning

Gaussian process regression model

PtAu

Icosahedral Pt Au nanoparticles

Density Functional Theory

GPAW

Effective Medium Theory

Supplementary materials

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Now Published

Genetic algorithms for computational materials discovery accelerated by machine learning

Paul C. Jennings, Steen Lysgaard, Jens Strabo Hummelshøj, Tejs Vegge, Thomas Bligaard journal article

npj Computational Materials , Volume 5, Issue 1

Online publication date: Apr 10, 2019

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Dec 10, 2018 Version 2

Dec 03, 2018 Version 1

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The content is available under CC BY 4.0

DOI

10.26434/chemrxiv.7411172.v2

Funding

FP7 Fuel Cells and Hydrogen Joint Technology Initiative grant agreement FP7-2012-JTI-FCH-325327

V-Sustain: The VILLUM Centre for the Science of Sustainable Fuels and Chemicals (no. 9455) from VILLUM FONDEN.

Author’s competing interest statement

No conflict

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

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Abstract

Keywords

Supplementary materials

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Now Published

Version History

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DOI

Funding

Author’s competing interest statement

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