Genetic algorithms for computational materials discovery accelerated by machine learning
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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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Optimization of a single property is feasible with either direct experimentation or physics-based modeling but can often be accelerated with machine learning.
“Optimization of a single property is feasible with either direct experimentation (117, 118) or physics-based modeling (119) but can often be accelerated with ML (43, 120, 121 ).”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [121]
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Self-citation
- 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
- Venue
- npj Computational Materials
- Published
- 2019-04-10
- Volume
- 5
- Issue
- 1
- ISSN
- 2057-3960 (ISSN-L)
- 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
- 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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