Machine learning for molecular simulations of crystal nucleation and growth

Molecular simulations are a powerful tool in the study of crystallization and polymorphic transitions yielding detailed information of transformation mechanisms with high spatiotemporal resolution. However, characterizing various crystalline and amorphous phases as well as sampling nucleation events...

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Bibliographic Details
Published in:MRS bulletin 2022-09, Vol.47 (9), p.949-957
Main Authors: Sarupria, Sapna, Hall, Steven W., Rogal, Jutta
Format: Article
Language:English
Subjects:
Applied and Technical Physics
Characterization and Evaluation of Materials
Chemistry and Materials Science
Crystal growth
Crystallization
Energy Materials
Machine learning
Materials Engineering
Materials Science
Nanotechnology
Nucleation
Physics
Review Article
Sampling
Simulation
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Summary:Molecular simulations are a powerful tool in the study of crystallization and polymorphic transitions yielding detailed information of transformation mechanisms with high spatiotemporal resolution. However, characterizing various crystalline and amorphous phases as well as sampling nucleation events and structural transitions remain extremely challenging tasks. The integration of machine learning with molecular simulations has the potential of unprecedented advancement in the area of crystal nucleation and growth. In this article, we discuss recent progress in the analysis and sampling of structural transformations aided by machine learning and the resulting potential future directions opening in this area. Graphical Abstract
ISSN:0883-7694
1938-1425
DOI:10.1557/s43577-022-00407-1