Investigating the quasi-liquid layer on ice surfaces: a comparison of order parameters.
Investigating the quasi-liquid layer on ice surfaces: a comparison of order parameters.
复制标题
研究冰表面的准液体层：有序参数的比较。
DOI：
10.1039/d2cp00752e
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Shi J
中科院分区：
文献类型：
--
作者：
Shi J
冰表面的特征在于预熔化的准液体层（QLL），其介导晶体生长过程和与外部试剂的相互作用。在分子水平上理解QLL对于解开冰晶形成的机制是必要的。QLL的计算研究在很大程度上依赖于用于识别局部分子环境和排列、区分固体状和液体状水分子的方法的准确性。在这里，我们比较使用不同的顺序参数，以表征六方冰（Ih）和立方冰（Ic）模型表面上的QLL的分子动力学（MD）模拟在一定温度范围内的研究所获得的结果。对于分类任务，除了不同风格的传统Steinhardt序参数外，我们还选择了熵指纹和深度学习神经网络方法（DeepIce），这是概念上不同的方法。我们发现，所有的分析方法给出定性相似的趋势的QLL的行为在冰面上的温度，与一些细微的差异，在分类的灵敏度限于固-液界面。随着温度的升高，冰面上QLL的厚度逐渐增加。的QLL大小的趋势和作为温度的函数的顺序参数的值为不同的方面可能会被链接到表面生长速率，这反过来又影响在较低的蒸汽压下的晶体形态。序参量的选择因此可以通过计算的便利性来通知，除非在非常精确地确定液-固界面是重要的情况下。
Ice surfaces are characterized by pre-melted quasi-liquid layers (QLLs), which mediate both crystal growth processes and interactions with external agents. Understanding QLLs at the molecular level is necessary to unravel the mechanisms of ice crystal formation. Computational studies of the QLLs heavily rely on the accuracy of the methods employed for identifying the local molecular environment and arrangements, discriminating between solid-like and liquid-like water molecules. Here we compare the results obtained using different order parameters to characterize the QLLs on hexagonal ice (Ih) and cubic ice (Ic) model surfaces investigated with molecular dynamics (MD) simulations in a range of temperatures. For the classification task, in addition to the traditional Steinhardt order parameters in different flavours, we select an entropy fingerprint and a deep learning neural network approach (DeepIce), which are conceptually different methodologies. We find that all the analysis methods give qualitatively similar trends for the behaviours of the QLLs on ice surfaces with temperature, with some subtle differences in the classification sensitivity limited to the solid–liquid interface. The thickness of QLLs on the ice surface increases gradually as the temperature increases. The trends of the QLL size and of the values of the order parameters as a function of temperature for the different facets may be linked to surface growth rates which, in turn, affect crystal morphologies at lower vapour pressure. The choice of the order parameter can be therefore informed by computational convenience except in cases where a very accurate determination of the liquid–solid interface is important.