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Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

10 December 2018, Version 2
Working Paper
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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Version History

Dec 10, 2018 Version 2

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The content is available under CC BY 4.0 [opens in a new tab]

DOI

10.26434/chemrxiv.7411172.v2
D O I: 10.26434/chemrxiv.7411172.v2 [opens in a new tab]

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

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