The MLIP package: moment tensor potentials with MPI and active learning
The subject of this paper is the technology (the 'how') of constructing machine-learning interatomic potentials, rather than science (the 'what' and 'why') of atomistic simulations using machine-learning potentials. Namely, we illustrate how to construct moment tensor p...
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| Published in | Machine learning: science and technology Vol. 2; no. 2; pp. 25002 - 25020 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Bristol
IOP Publishing
01.06.2021
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| Subjects | |
| Online Access | Get full text |
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| Abstract | The subject of this paper is the technology (the 'how') of constructing machine-learning interatomic potentials, rather than science (the 'what' and 'why') of atomistic simulations using machine-learning potentials. Namely, we illustrate how to construct moment tensor potentials using active learning as implemented in the MLIP package, focusing on the efficient ways to automatically sample configurations for the training set, how expanding the training set changes the error of predictions, how to set up ab initio calculations in a cost-effective manner, etc. The MLIP package (short for Machine-Learning Interatomic Potentials) is available at https://mlip.skoltech.ru/download/. |
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| AbstractList | The subject of this paper is the technology (the ‘how’) of constructing machine-learning interatomic potentials, rather than science (the ‘what’ and ‘why’) of atomistic simulations using machine-learning potentials. Namely, we illustrate how to construct moment tensor potentials using active learning as implemented in the MLIP package, focusing on the efficient ways to automatically sample configurations for the training set, how expanding the training set changes the error of predictions, how to set up ab initio calculations in a cost-effective manner, etc. The MLIP package (short for Machine-Learning Interatomic Potentials) is available at https://mlip.skoltech.ru/download/. |
| Author | Shapeev, Alexander V Gubaev, Konstantin Podryabinkin, Evgeny V Novikov, Ivan S |
| Author_xml | – sequence: 1 givenname: Ivan S surname: Novikov fullname: Novikov, Ivan S organization: Skolkovo Institute of Science and Technology, Skolkovo Innovation Center , Nobel St. 3, Moscow 143026, Russian Federation – sequence: 2 givenname: Konstantin surname: Gubaev fullname: Gubaev, Konstantin organization: Delft University of Technology Department of Materials Science and Engineering, Mekelweg 2, 2628 CD Delft, Netherlands – sequence: 3 givenname: Evgeny V surname: Podryabinkin fullname: Podryabinkin, Evgeny V organization: Skolkovo Institute of Science and Technology, Skolkovo Innovation Center , Nobel St. 3, Moscow 143026, Russian Federation – sequence: 4 givenname: Alexander V orcidid: 0000-0002-7497-5594 surname: Shapeev fullname: Shapeev, Alexander V email: a.shapeev@skoltech.ru organization: Skolkovo Institute of Science and Technology, Skolkovo Innovation Center , Nobel St. 3, Moscow 143026, Russian Federation |
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| CODEN | MLSTCK |
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| ContentType | Journal Article |
| Copyright | 2020 The Author(s). Published by IOP Publishing Ltd 2021. This work is published under http://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: 2020 The Author(s). Published by IOP Publishing Ltd – notice: 2021. This work is published under http://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| DocumentTitleAlternate | The MLIP package: moment tensor potentials with MPI and active learning |
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| References | 44 45 46 47 48 49 Mortazavi B (57) 2020; 3 Pozdnyakov S N (17) 2020 Rosenbrock C W (83) 2019 Schütt K (10) 2017 50 51 Sivaraman G (70) 2019 52 53 54 11 55 12 56 13 14 58 15 59 16 18 19 1 2 Plimpton S (75) 1993 3 4 5 6 7 8 9 Novikov I S (71) 60 61 62 63 20 64 21 22 66 67 24 68 25 69 26 27 28 29 Novikov I S (72) 30 74 31 32 76 33 Settles B (65) 2012; 6 77 34 78 35 79 36 37 39 Gilmer J (38) 2017 80 81 82 van der Oord C (23) 2020; 1 40 41 42 43 Novikov I S (73) |
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| Snippet | The subject of this paper is the technology (the 'how') of constructing machine-learning interatomic potentials, rather than science (the 'what' and 'why') of... The subject of this paper is the technology (the ‘how’) of constructing machine-learning interatomic potentials, rather than science (the ‘what’ and ‘why’) of... |
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| Title | The MLIP package: moment tensor potentials with MPI and active learning |
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