The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Accelerating high-throughput searches for new alloys with active learning of interatomic potentials
Open accessAbstract
We propose an approach to materials prediction that uses a machine-learning interatomic potential to approximate quantum-mechanical energies and an active learning algorithm for the automatic selection of an optimal training dataset. Our approach significantly reduces the amount of DFT calculations needed, resorting to DFT only to produce the training data, while structural optimization is performed using the interatomic potentials. Our approach is not limited to one (or a small number of) lattice types (as is the case for cluster expansion, for example) and can predict structures with lattice types not present in the training dataset. We demonstrate the effectiveness of our algorithm by predicting the convex hull for the following three systems: Cu-Pd, Co-Nb-V, and Al-Ni-Ti. Our method is three to four orders of magnitude faster than conventional high-throughput DFT calculations and explores a wider range of materials space. In all three systems, we found unreported stable structures compared to the AFLOW database. Because our method is much cheaper and explores much more of materials space than high-throughput methods or cluster expansion, and because our interatomic potentials have a systematically improvable accuracy compared to empirical potentials such as EAM, etc., it will have a significant impact in the discovery of new alloy phases, particularly those with three or more components.
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Cites
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
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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
Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
Bartók, A. P., Payne, M. C., Kondor, R., & Csányi, G. (2010) · Physical Review Letters · doi:10.1103/physrevlett.104.136403 · Cited by 3,242
Saal, J. E., Kirklin, S., Aykol, M., Meredig, B., & Wolverton, C. (2013) · JOM · doi:10.1007/s11837-013-0755-4 · Cited by 2,584
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
Smith, J. S., Isayev, O., & Roitberg, A. E. (2017) · Chemical Science · doi:10.1039/c6sc05720a · Cited by 1,921
A general-purpose machine learning framework for predicting properties of inorganic materials
Ward, L., Agrawal, A., Choudhary, A., & Wolverton, C. (2016) · npj Computational Materials · doi:10.1038/npjcompumats.2016.28 · Cited by 1,831
AFLOW: An automatic framework for high-throughput materials discovery
Curtarolo, S., Setyawan, W., Hart, G. L. W., Jahnatek, M., Chepulskii, R. V., Taylor, R. H., Wang, S., Xue, J., Yang, K., Levy, O., et al. (2012) · Computational Materials Science · doi:10.1016/j.commatsci.2012.02.005 · Cited by 1,531
Self-citationMoment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials
Shapeev, A. V. (2016) · Multiscale Modeling & Simulation · doi:10.1137/15m1054183 · Cited by 1,510
Self-citationNew developments in evolutionary structure prediction algorithm USPEX
Lyakhov, A. O., Oganov, A. R., Stokes, H. T., & Zhu, Q. (2013) · Computer Physics Communications · doi:10.1016/j.cpc.2012.12.009 · Cited by 1,394
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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
Self-citationHart, G. L. W., Mueller, T., Toher, C., & Curtarolo, S. (2021) · Nature Reviews Materials · doi:10.1038/s41578-021-00340-w · Cited by 703
Self-citationThe MLIP package: moment tensor potentials with MPI and active learning
Novikov, I. S., Gubaev, K., Podryabinkin, E. V., & Shapeev, A. V. (2021) · Machine Learning: Science and Technology · doi:10.1088/2632-2153/abc9fe · Cited by 587
Self-citationOn-the-fly active learning of interpretable Bayesian force fields for atomistic rare events
Vandermause, J., Torrisi, S. B., Batzner, S., Xie, Y., Sun, L., Kolpak, A. M., & Kozinsky, B. (2020) · npj Computational Materials · doi:10.1038/s41524-020-0283-z · Cited by 459
Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide
Sivaraman, G., Krishnamoorthy, A. N., Baur, M., Holm, C., Stan, M., Csányi, G., Benmore, C., & Vázquez-Mayagoitia, Á. (2020) · npj Computational Materials · doi:10.1038/s41524-020-00367-7 · Cited by 194
Metadata
- Authors
- Gubaev, K., Podryabinkin, E. V., Hart, G. L. W., & Shapeev, A. V.
- Venue
- Computational Materials Science
- Publisher
- Elsevier BV
- Published
- 2019-01
- Volume
- 156
- Pages
- 148-156
- ISSN
- 0927-0256 (ISSN-L)
- OpenAlex
- W2809779474
- Cited by
- 359
- References
- 46
- FWCI
- 10.30
- Type
- journal article
- Language
- en
- Open access
- bronze
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