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Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

preprint
revised on 10.12.2018, 11:15 and posted on 10.12.2018, 20:15 by Steen Lysgaard, Paul C. Jennings, Jens Strabo Hummelshøj, Thomas Bligaard, Tejs Vegge
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.

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.

History

Email Address of Submitting Author

stly@dtu.dk

Institution

Technical University of Denmark

Country

Denmark

ORCID For Submitting Author

0000-0002-2032-8949

Declaration of Conflict of Interest

No conflict

Licence

Exports