Physical Review B, volume 99, issue 6, publication number 064114
Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning
Publication type: Journal Article
Publication date: 2019-02-27
Journal:
Physical Review B
Quartile SCImago
Q1
Quartile WOS
Q2
Impact factor: 3.7
ISSN: 24699950, 24699969, 10980121, 1550235X
Abstract
We propose a methodology for crystal structure prediction that is based on the evolutionary algorithm USPEX and the machine-learning interatomic potentials actively learning on-the-fly. Our methodology allows for an automated construction of an interatomic interaction model from scratch, replacing the expensive density functional theory (DFT) and giving a speedup of several orders of magnitude. Predicted low-energy structures are then tested on DFT, ensuring that our machine-learning model does not introduce any prediction error. We tested our methodology on prediction of crystal structures of carbon, high-pressure phases of sodium, and boron allotropes, including those that have more than 100 atoms in the primitive cell. All the the main allotropes have been reproduced, and a hitherto unknown 54-atom structure of boron has been predicted with very modest computational effort.
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Citations by publishers
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Elsevier
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Taylor & Francis
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1 publication, 0.45%
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Public Library of Science (PLoS)
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Public Library of Science (PLoS)
1 publication, 0.45%
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IEEE
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IEEE
1 publication, 0.45%
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Hindawi Limited
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1 publication, 0.45%
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Oxford University Press
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1 publication, 0.45%
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Proceedings of the National Academy of Sciences (PNAS)
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Proceedings of the National Academy of Sciences (PNAS)
1 publication, 0.45%
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- We do not take into account publications that without a DOI.
- Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
- Statistics recalculated weekly.
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Podryabinkin E. V. et al. Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning // Physical Review B. 2019. Vol. 99. No. 6. 064114
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Podryabinkin E. V., Tikhonov E. V., Shapeev A. V., Oganov A. R. Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning // Physical Review B. 2019. Vol. 99. No. 6. 064114
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TY - JOUR
DO - 10.1103/PhysRevB.99.064114
UR - https://doi.org/10.1103%2FPhysRevB.99.064114
TI - Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning
T2 - Physical Review B
AU - Podryabinkin, Evgeny V.
AU - Tikhonov, Evgeny V.
AU - Shapeev, Alexander V.
AU - Oganov, A. R.
PY - 2019
DA - 2019/02/27 00:00:00
PB - American Physical Society (APS)
IS - 6
VL - 99
SN - 2469-9950
SN - 2469-9969
SN - 1098-0121
SN - 1550-235X
ER -
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@article{2019_Podryabinkin
author = {Evgeny V. Podryabinkin and Evgeny V. Tikhonov and Alexander V. Shapeev and A. R. Oganov},
title = {Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning},
journal = {Physical Review B},
year = {2019},
volume = {99},
publisher = {American Physical Society (APS)},
month = {feb},
url = {https://doi.org/10.1103%2FPhysRevB.99.064114},
number = {6},
doi = {10.1103/PhysRevB.99.064114}
}
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