Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning

Machine learning a defect’s effect A method for quickly predicting the dominant equilibrium atomic-level defects in a material is developed by researchers in the USA. Crystalline materials derive many of their attributes from the regular and symmetric arrangement of their atoms. Consequently, a miss...

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Autores principales: Bharat Medasani, Anthony Gamst, Hong Ding, Wei Chen, Kristin A Persson, Mark Asta, Andrew Canning, Maciej Haranczyk
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2016
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Acceso en línea:https://doaj.org/article/a32c20d9819043b39e1bf57b270fa447
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