Uncovering the effects of interface-induced ordering of liquid on crystal growth using machine learning

Crystallization is a challenging process to model quantitatively. Here the authors use machine learning and atomistic simulations together to uncover the role of the liquid structure on the process of crystallization and derive a predictive kinetic model of crystal growth.

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Detalles Bibliográficos
Autores principales: Rodrigo Freitas, Evan J. Reed
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2020
Materias:
Q
Acceso en línea:https://doaj.org/article/964c406f580d4fc48ff08ec6be350803
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