Cited by 270Machine Learning in Materials ScienceAdvanced Chemical Physics StudiesEvolutionary Algorithms and Applications

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Genetic algorithms for computational materials discovery accelerated by machine learning

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Jennings, Paul C., Lysgaard, Steen, Hummelshøj, Jens Strabo, Vegge, Tejs, Bligaard, Thomas·2019·npj Computational Materials

DOI 10.1038/s41524-019-0181-4

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Abstract

Materials discovery is increasingly being impelled by machine learning methods that rely on pre-existing datasets. Where datasets are lacking, unbiased data generation can be achieved with genetic algorithms. Here a machine learning model is trained on-the-fly as a computationally inexpensive energy predictor before analyzing how to augment convergence in genetic algorithm-based approaches by using the model as a surrogate. This leads to a machine learning accelerated genetic algorithm combining robust qualities of the genetic algorithm with rapid machine learning. The approach is used to search for stable, compositionally variant, geometrically similar nanoparticle alloys to illustrate its capability for accelerated materials discovery, e.g., nanoalloy catalysts. The machine learning accelerated approach, in this case, yields a 50-fold reduction in the number of required energy calculations compared to a traditional “brute force” genetic algorithm. This makes searching through the space of all homotops and compositions of a binary alloy particle in a given structure feasible, using density functional theory calculations.

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Authors: Jennings, P. C., Lysgaard, S., Hummelshøj, J. S., Vegge, T., & Bligaard, T.
Affiliations: SLAC National Accelerator Laboratory (US)
Technical University of Denmark (DK)
Toyota Research Institute (US)
Corresponding author: Vegge, Tejs
ORCID: Lysgaard, Steen
Hummelshøj, Jens Strabo
Vegge, Tejs
Bligaard, Thomas
Venue: npj Computational Materials
Publisher: Springer Science and Business Media LLC
Published: 2019-04-10
Volume: 5
Issue: 1
ISSN: 2057-3960 (ISSN-L)
DOI: 10.1038/s41524-019-0181-4
OpenAlex: W2939893088
Cited by: 269
References: 51
Citations by year: 2026: 36 · 2025: 42 · 2024: 35 · 2023: 39 · 2022: 44 · 2021: 37 · 2020: 29 · 2019: 7
FWCI: 10.65
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
Locations: npj Computational Materials (publishedVersion, cc-by)
ChemRxiv (acceptedVersion, cc-by)
OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Royal College of Surgeons in Ireland (submittedVersion, cc-by)
Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, cc-by)
Funders: U.S. Department of Energy
Villum Fonden
Toyota Research Institute
European Commission (325327)
Topics: Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
Keywords: genetic algorithm, machine learning acceleration, materials discovery, surrogate models, energy prediction, density functional theory, computational complexity reduction

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Genetic algorithms for computational materials discovery accelerated by machine learning

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