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

The DOI matches its Crossref record; 48 of the 51 references it cites were found in a registry or an index.

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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  1. Holland, J. H. Adaptation in Natural and Artificial Systems (The University of Michigan Press, Ann Arbor, MI, 1975) p. 211.
  2. Rasmussen, C. E. & Williams, C. K. I. Gaussian Processes for Machine Learning (MIT Press, Cambridge, MA, 2006) p. 248. · DOI · Paper page

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Metadata

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
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
Licence
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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