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.
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2020-06-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-020-16892-4 |
| Summary: | 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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| ISSN: | 2041-1723 |