Journal of Chemical Theory and Computation, volume 18, issue 2, pages 1109-1121
Nanohardness from First Principles with Active Learning on Atomic Environments
Asgarpour Milad
1, 2
,
Maslenikov Igor I.
3
,
Ovsyannikov Danila A
3
,
Sorokin Pavel B.
3, 4
,
Popov Mikhail Yu
3, 4
,
Shapeev Alexander V
1
2
University of Limerick, Limerick V94 T9PX, Ireland
|
Publication type: Journal Article
Publication date: 2022-01-06
Quartile SCImago
Q1
Quartile WOS
Q1
Impact factor: 5.5
ISSN: 15499618, 15499626
Physical and Theoretical Chemistry
Computer Science Applications
Abstract
We propose a methodology for the calculation of nanohardness by atomistic simulations of nanoindentation. The methodology is enabled by machine-learning interatomic potentials fitted on the fly to quantum-mechanical calculations of local fragments of the large nanoindentation simulation. We test our methodology by calculating nanohardness, as a function of load and crystallographic orientation of the surface, of diamond, AlN, SiC, BC2N, and Si and comparing it to the calibrated values of the macro- and microhardness. The observed agreement between the computational and experimental results from the literature provides evidence that our method has sufficient predictive power to open up the possibility of designing materials with exceptional hardness directly from first principles. It will be especially valuable at the nanoscale where the experimental measurements are difficult, while empirical models fitted to macrohardness are, as a rule, inapplicable.
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Citations by publishers
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2
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American Physical Society (APS)
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2 publications, 20%
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Springer Nature
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Springer Nature
2 publications, 20%
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Wiley
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2 publications, 20%
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1 publication, 10%
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American Institute of Physics (AIP)
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American Institute of Physics (AIP)
1 publication, 10%
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Taylor & Francis
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Taylor & Francis
1 publication, 10%
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Elsevier
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Elsevier
1 publication, 10%
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1
2
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Podryabinkin E. V. et al. Nanohardness from First Principles with Active Learning on Atomic Environments // Journal of Chemical Theory and Computation. 2022. Vol. 18. No. 2. pp. 1109-1121.
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Podryabinkin E. V., Kvashnin A. G., Asgarpour M., Maslenikov I. I., Ovsyannikov D. A., Sorokin P. B., Popov M. Yu., Shapeev A. V. Nanohardness from First Principles with Active Learning on Atomic Environments // Journal of Chemical Theory and Computation. 2022. Vol. 18. No. 2. pp. 1109-1121.
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TY - JOUR
DO - 10.1021/acs.jctc.1c00783
UR - https://doi.org/10.1021%2Facs.jctc.1c00783
TI - Nanohardness from First Principles with Active Learning on Atomic Environments
T2 - Journal of Chemical Theory and Computation
AU - Asgarpour, Milad
AU - Maslenikov, Igor I.
AU - Ovsyannikov, Danila A
AU - Shapeev, Alexander V
AU - Popov, Mikhail Yu
AU - Podryabinkin, Evgeny V.
AU - Kvashnin, Alexander G.
AU - Sorokin, Pavel B.
PY - 2022
DA - 2022/01/06 00:00:00
PB - American Chemical Society (ACS)
SP - 1109-1121
IS - 2
VL - 18
SN - 1549-9618
SN - 1549-9626
ER -
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@article{2022_Podryabinkin,
author = {Milad Asgarpour and Igor I. Maslenikov and Danila A Ovsyannikov and Alexander V Shapeev and Mikhail Yu Popov and Evgeny V. Podryabinkin and Alexander G. Kvashnin and Pavel B. Sorokin},
title = {Nanohardness from First Principles with Active Learning on Atomic Environments},
journal = {Journal of Chemical Theory and Computation},
year = {2022},
volume = {18},
publisher = {American Chemical Society (ACS)},
month = {jan},
url = {https://doi.org/10.1021%2Facs.jctc.1c00783},
number = {2},
pages = {1109--1121},
doi = {10.1021/acs.jctc.1c00783}
}
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Podryabinkin, Evgeny V., et al. “Nanohardness from First Principles with Active Learning on Atomic Environments.” Journal of Chemical Theory and Computation, vol. 18, no. 2, Jan. 2022, pp. 1109-1121. https://doi.org/10.1021%2Facs.jctc.1c00783.