Machine learning for molecular simulations of crystal nucleation and growth
Machine learning for molecular simulations of crystal nucleation and growth
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DOI：
10.1557/s43577-022-00407-1
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发表时间：
2022-09
期刊：
影响因子：
5
通讯作者：
Sapna Sarupria;Steven W Hall;J. Rogal
中科院分区：
文献类型：
--
作者：
Sapna Sarupria;Steven W Hall;J. Rogal
摘要分子模拟是研究结晶和多晶型转变的有力工具，它能提供高时空分辨率的转变机制的详细信息。然而，表征各种结晶和非晶相以及采样成核事件和结构转变仍然是极具挑战性的任务。机器学习与分子模拟的集成在晶体成核和生长领域具有前所未有的进步潜力。在这篇文章中，我们讨论了在机器学习辅助下的结构转换分析和采样方面的最新进展，以及由此产生的潜在未来方向。图形摘要
Abstract 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