# Genetic algorithms for computational materials discovery accelerated by machine learning

Article · Paul C. Jennings, Steen Lysgaard, Jens S. Hummelshøj, Tejs Vegge, Thomas Bligaard

- Archive id: doi:10.1038/s41524-019-0181-4
- Published: 2019-04-10
- Venue: npj Computational Materials
- DOI: https://doi.org/10.1038/s41524-019-0181-4
- Citations: 269
- Source URL: https://doi.org/10.1038/s41524-019-0181-4
- PDF: https://www.nature.com/articles/s41524-019-0181-4.pdf
- Provenance: openalex

## 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.

_Abstract reproduced under CC-BY (publisher license, via OpenAlex)._

Topics: [AI for Science](https://space-kitty.com/topics/ai-for-science)

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