Predicting the propensity for thermally activated β events in metallic glasses via interpretable machine learning

Abstract The elementary excitations in metallic glasses (MGs), i.e., β processes that involve hopping between nearby sub-basins, underlie many unusual properties of the amorphous alloys. A high-efficacy prediction of the propensity for those activated processes from solely the atomic positions, howe...

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Autores principales: Qi Wang, Jun Ding, Longfei Zhang, Evgeny Podryabinkin, Alexander Shapeev, Evan Ma
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2020
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Acceso en línea:https://doaj.org/article/607c7977330949b6958952bb11789513
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