Uncovering the effects of interface-induced ordering of liquid on crystal growth using machine learning
Uncovering the effects of interface-induced ordering of liquid on crystal growth using machine learning
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DOI：
10.1038/s41467-020-16892-4
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发表时间：
2020-06-26
影响因子：
16.6
通讯作者：
Reed, Evan J.
中科院分区：
文献类型：
--
作者：
Freitas, Rodrigo;Reed, Evan J.
结晶过程通常被理解为形成晶体的基本微观结构元素，如表面取向或缺陷的存在。对于液体结构在晶体生长动力学中的作用，人们所知甚少。本文采用原子模拟和机器学习方法共同证明了固液界面附近的液体呈现出明显的结构有序，这有效地降低了原子的迁移率，减缓了结晶动力学。通过对硅和铜的详细研究，我们发现界面诱导有序（IIO）对液体迁移率的影响程度随相邻界面的有序程度和性质而有很大差异。解释了IIO各向异性背后的物理机制，并证明了将这种效应纳入物理驱动的晶体生长模型可以定量预测生长速率的温度依赖性。结晶是一个具有挑战性的过程，以定量建模。在这里，作者使用机器学习和原子模拟一起揭示了液体结构在结晶过程中的作用，并推导了晶体生长的预测动力学模型。
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. 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.