Machine learning for molecular simulations of crystal nucleation and growth

نویسندگان

چکیده

Molecular simulations are a powerful tool in the study of crystallization and polymorphic transitions yielding detailed information transformation mechanisms with high spatiotemporal resolution. However, characterizing various crystalline amorphous phases as well sampling nucleation events structural remain extremely challenging tasks. The integration machine learning molecular has potential unprecedented advancement area crystal growth. In this article, we discuss recent progress analysis transformations aided by resulting future directions opening area.

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ژورنال

عنوان ژورنال: Mrs Bulletin

سال: 2022

ISSN: ['1938-1425', '0883-7694']

DOI: https://doi.org/10.1557/s43577-022-00407-1