Machine-learning interatomic potential for radiation damage and defects in tungsten

Tutkimustuotos: ArtikkelijulkaisuArtikkeliTieteellinenvertaisarvioitu

Kuvaus

We introduce a machine-learning interatomic potential for tungsten using the Gaussian approximation potential framework. We specifically focus on properties relevant for simulations of radiation-induced collision cascades and the damage they produce, including a realistic repulsive potential for the short-range many-body cascade dynamics and a good description of the liquid phase. Furthermore, the potential accurately reproduces surface properties and the energetics of vacancy and self-interstitial clusters, which have been longstanding deficiencies of existing potentials. The potential enables molecular dynamics simulations of radiation damage in tungsten with unprecedented accuracy.
Alkuperäiskielienglanti
Artikkeli144105
LehtiPhysical Review B
Vuosikerta100
Numero14
Sivumäärä15
ISSN2469-9950
DOI - pysyväislinkit
TilaJulkaistu - 17 lokakuuta 2019
OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä, vertaisarvioitu

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  • 114 Fysiikka

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title = "Machine-learning interatomic potential for radiation damage and defects in tungsten",
abstract = "We introduce a machine-learning interatomic potential for tungsten using the Gaussian approximation potential framework. We specifically focus on properties relevant for simulations of radiation-induced collision cascades and the damage they produce, including a realistic repulsive potential for the short-range many-body cascade dynamics and a good description of the liquid phase. Furthermore, the potential accurately reproduces surface properties and the energetics of vacancy and self-interstitial clusters, which have been longstanding deficiencies of existing potentials. The potential enables molecular dynamics simulations of radiation damage in tungsten with unprecedented accuracy.",
keywords = "APPROXIMATION, CASCADE DAMAGE, IN-SITU, INITIO MOLECULAR-DYNAMICS, RECOVERY, SEMICONDUCTORS, SIMULATIONS, TOTAL-ENERGY CALCULATIONS, TRANSITION, VACANCIES, 114 Physical sciences",
author = "Jesper Byggm{\"a}star and Ali Hamedani and Kai Nordlund and Flyura Djurabekova",
year = "2019",
month = "10",
day = "17",
doi = "10.1103/PhysRevB.100.144105",
language = "English",
volume = "100",
journal = "Physical Review B",
issn = "2469-9950",
publisher = "American Physical Society",
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Machine-learning interatomic potential for radiation damage and defects in tungsten. / Byggmästar, Jesper; Hamedani, Ali; Nordlund, Kai; Djurabekova, Flyura.

julkaisussa: Physical Review B, Vuosikerta 100, Nro 14, 144105, 17.10.2019.

Tutkimustuotos: ArtikkelijulkaisuArtikkeliTieteellinenvertaisarvioitu

TY - JOUR

T1 - Machine-learning interatomic potential for radiation damage and defects in tungsten

AU - Byggmästar, Jesper

AU - Hamedani, Ali

AU - Nordlund, Kai

AU - Djurabekova, Flyura

PY - 2019/10/17

Y1 - 2019/10/17

N2 - We introduce a machine-learning interatomic potential for tungsten using the Gaussian approximation potential framework. We specifically focus on properties relevant for simulations of radiation-induced collision cascades and the damage they produce, including a realistic repulsive potential for the short-range many-body cascade dynamics and a good description of the liquid phase. Furthermore, the potential accurately reproduces surface properties and the energetics of vacancy and self-interstitial clusters, which have been longstanding deficiencies of existing potentials. The potential enables molecular dynamics simulations of radiation damage in tungsten with unprecedented accuracy.

AB - We introduce a machine-learning interatomic potential for tungsten using the Gaussian approximation potential framework. We specifically focus on properties relevant for simulations of radiation-induced collision cascades and the damage they produce, including a realistic repulsive potential for the short-range many-body cascade dynamics and a good description of the liquid phase. Furthermore, the potential accurately reproduces surface properties and the energetics of vacancy and self-interstitial clusters, which have been longstanding deficiencies of existing potentials. The potential enables molecular dynamics simulations of radiation damage in tungsten with unprecedented accuracy.

KW - APPROXIMATION

KW - CASCADE DAMAGE

KW - IN-SITU

KW - INITIO MOLECULAR-DYNAMICS

KW - RECOVERY

KW - SEMICONDUCTORS

KW - SIMULATIONS

KW - TOTAL-ENERGY CALCULATIONS

KW - TRANSITION

KW - VACANCIES

KW - 114 Physical sciences

U2 - 10.1103/PhysRevB.100.144105

DO - 10.1103/PhysRevB.100.144105

M3 - Article

VL - 100

JO - Physical Review B

JF - Physical Review B

SN - 2469-9950

IS - 14

M1 - 144105

ER -