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
Open accessPublished version of a preprint
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Goldberg, D. E., & Shakespeare, W. (2002) · Cited by 16,088
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Cristianini, N., & Shawe-Taylor, J. (2000) · doi:10.1017/cbo9780511801389 · Cited by 13,855
Hammer, B., Hansen, L. B., & Nørskov, J. K. (1999) · Physical Review B · doi:10.1103/physrevb.59.7413 · Cited by 8,270
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
Behler, J., & Parrinello, M. (2007) · Physical Review Letters · doi:10.1103/physrevlett.98.146401 · Cited by 5,010
The atomic simulation environment—a Python library for working with atoms
Larsen, A. H., Mortensen, J. J., Blomqvist, J., Castelli, I. E., Christensen, R., Dułak, M., Friis, J., Groves, M. N., Hammer, B., Hargus, C., et al. (2017) · Journal of Physics Condensed Matter · doi:10.1088/1361-648x/aa680e · Cited by 4,790
Self-citationNanoalloys: From Theory to Applications of Alloy Clusters and Nanoparticles
Ferrando, R., Jellinek, J., & Johnston, R. L. (2008) · Chemical Reviews · doi:10.1021/cr040090g · Cited by 3,690
On representing chemical environments
Bartók, A. P., Kondor, R., & Csányi, G. (2013) · Physical Review B · doi:10.1103/physrevb.87.184115 · Cited by 2,572
Enkovaara, J., Rostgaard, C., Mortensen, J. J., Chen, J., Dułak, M., Ferrighi, L., Gavnholt, J., Glinsvad, C., Haikola, V., Hansen, H. A., et al. (2010) · Journal of Physics Condensed Matter · doi:10.1088/0953-8984/22/25/253202 · Cited by 2,239
Structural Relaxation Made Simple
Bitzek, E., Koskinen, P., Franz Gähler, A., Moseler, M., & Gumbsch, P. (2006) · Physical Review Letters · doi:10.1103/physrevlett.97.170201 · Cited by 1,766
Long-range Finnis–Sinclair potentials
Sutton, A. P., & Chen, J. (1990) · Philosophical Magazine Letters · doi:10.1080/09500839008206493 · Cited by 1,182
Research works printed but not linked: 2
- Holland, J. H. Adaptation in Natural and Artificial Systems (The University of Michigan Press, Ann Arbor, MI, 1975) p. 211.
- Rasmussen, C. E. & Williams, C. K. I. Gaussian Processes for Machine Learning (MIT Press, Cambridge, MA, 2006) p. 248. · DOI · Paper page
Cited by
Sorted by citations
Artificial Intelligence Applied to Battery Research: Hype or Reality?
Lombardo, T., Duquesnoy, M., El-Bouysidy, H., Årén, F., Gallo‐Bueno, A., Jørgensen, P. B., Bhowmik, A., Demortière, A., Ayerbe, E., Alcaide, F., et al. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00108 · Cited by 536
Self-citationInterpretable machine learning for knowledge generation in heterogeneous catalysis
Esterhuizen, J. A., Goldsmith, B. R., & Linic, S. (2022) · Nature Catalysis · doi:10.1038/s41929-022-00744-z · Cited by 442
Deep Learning in Mechanical Metamaterials: From Prediction and Generation to Inverse Design
Zheng, X., Zhang, X., Chen, T., & Watanabe, I. (2023) · Advanced Materials · doi:10.1002/adma.202302530 · Cited by 407
Machine learning for interatomic potential models
Mueller, T., Hernández, A., & Wang, C. (2020) · The Journal of Chemical Physics · doi:10.1063/1.5126336 · Cited by 380
Nandy, A., Duan, C., Taylor, M. G., Liu, F., Steeves, A. H., & Kulik, H. J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00347 · Cited by 358
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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
