Efficient and transferable machine learning potentials for the simulation of crystal defects in bcc Fe and W | Zendy

Alexandra M. Goryaeva | Zendy; Julien Dérès | Zendy; Clovis Lapointe | Zendy; Petr Grigorev | Zendy; Thomas D. Swinburne | Zendy; James R. Kermode | Zendy; Lisa Ventelon | Zendy; Jacopo Baima | Zendy; Mihai-Cosmin Marinica | Zendy

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Efficient and transferable machine learning potentials for the simulation of crystal defects in bcc Fe and W

Author(s) -

Alexandra M. Goryaeva,

Julien Dérès,

Clovis Lapointe,

Petr Grigorev,

Thomas D. Swinburne,

James R. Kermode,

Lisa Ventelon,

Jacopo Baima,

Mihai-Cosmin Marinica

Publication year - 2021

Publication title -

physical review materials

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.439

H-Index - 42

eISSN - 2476-0455

pISSN - 2475-9953

DOI - 10.1103/physrevmaterials.5.103803

Subject(s) - quadratic equation , stability (learning theory) , materials science , interatomic potential , transferability , space (punctuation) , representation (politics) , observable , flexibility (engineering) , dislocation , pseudopotential , statistical physics , computer science , condensed matter physics , physics , mathematics , machine learning , quantum mechanics , molecular dynamics , geometry , statistics , logit , politics , political science , law , operating system , composite material

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