Uncovering the effects of interface-induced ordering of liquid on crystal growth using machine learning.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
26 Jun 2020
26 Jun 2020
Historique:
received:
17
04
2020
accepted:
26
05
2020
entrez:
28
6
2020
pubmed:
28
6
2020
medline:
28
6
2020
Statut:
epublish
Résumé
The process of crystallization is often understood in terms of the fundamental microstructural elements of the crystallite being formed, such as surface orientation or the presence of defects. Considerably less is known about the role of the liquid structure on the kinetics of crystal growth. Here atomistic simulations and machine learning methods are employed together to demonstrate that the liquid adjacent to solid-liquid interfaces presents significant structural ordering, which effectively reduces the mobility of atoms and slows down the crystallization kinetics. Through detailed studies of silicon and copper we discover that the extent to which liquid mobility is affected by interface-induced ordering (IIO) varies greatly with the degree of ordering and nature of the adjacent interface. Physical mechanisms behind the IIO anisotropy are explained and it is demonstrated that incorporation of this effect on a physically-motivated crystal growth model enables the quantitative prediction of the growth rate temperature dependence.
Identifiants
pubmed: 32591501
doi: 10.1038/s41467-020-16892-4
pii: 10.1038/s41467-020-16892-4
pmc: PMC7319977
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
3260Subventions
Organisme : DOE | National Nuclear Security Administration (NNSA)
ID : DE-NA0002007
Organisme : National Science Foundation (NSF)
ID : CAREER-1455050
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