The DOI matches its Crossref record; 75 of the 75 references it cites were found in a registry or an index.
Achieving DFT accuracy with a machine-learning interatomic potential: Thermomechanics and defects in bcc ferromagnetic iron
Open accessAbstract
We show that the Gaussian Approximation Potential (GAP) machine-learning framework can describe complex magnetic potential energy surfaces, taking ferromagnetic iron as a paradigmatic challenging case. The training database includes total energies, forces, and stresses obtained from density-functional theory in the generalized-gradient approximation, and comprises approximately 150,000 local atomic environments, ranging from pristine and defected bulk configurations to surfaces and generalized stacking faults with different crystallographic orientations. We find the structural, vibrational, and thermodynamic properties of the GAP model to be in excellent agreement with those obtained directly from first-principles electronic-structure calculations. There is good transferability to quantities, such as Peierls energy barriers, which are determined to a large extent by atomic configurations that were not part of the training set. We observe the benefit and the need of using highly converged electronic-structure calculations to sample a target potential energy surface. The end result is a systematically improvable potential that can achieve the same accuracy of density-functional theory calculations, but at a fraction of the computational cost.
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Cited by
Performance and Cost Assessment of Machine Learning Interatomic Potentials
Zuo, Y., Chen, C., Li, X., Deng, Z., Chen, Y., Behler, J., Csányi, G., Shapeev, A. V., Thompson, A. P., Wood, M. A., et al. (2020) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.9b08723 · Cited by 968
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Physics-Inspired Structural Representations for Molecules and Materials
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Self-citationMachine Learning a General-Purpose Interatomic Potential for Silicon
Bartók, A. P., Kermode, J., Bernstein, N., & Csányi, G. (2018) · Physical Review X · doi:10.1103/physrevx.8.041048 · Cited by 470
Self-citationEmerging materials intelligence ecosystems propelled by machine learning
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Metadata
- Authors
- Dragoni, D., Daff, T. D., Csányi, G., & Marzari, N.
- Venue
- Physical Review Materials
- Publisher
- American Physical Society (APS)
- Published
- 2018-01-30
- Volume
- 2
- Issue
- 1
- ISSN
- 2475-9953 (ISSN-L)
- OpenAlex
- W2728984672
- Cited by
- 284
- References
- 75
- FWCI
- 12.20
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Dragoni, D., Daff, T. D., Csányi, G., & Marzari, N. (2018). Achieving DFT accuracy with a machine-learning interatomic potential: Thermomechanics and defects in bcc ferromagnetic iron. Physical Review Materials. https://doi.org/10.1103/physrevmaterials.2.013808
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