Cambridge Open Engage home
What is Cambridge Open Engage?
How to Submit
Browse
About
News [opens in a new tab]

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

Title
Description
Actions
Title
main
Description
Actions

Comments

Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.

Version History

Dec 10, 2018 Version 2

Metrics

4,166
1,067
0
Views
Downloads
Citations

License

CC logo
CC
BY logo
BY
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

Share