Doi Hideo, Takahashi Kazuaki Z, Aoyagi Takeshi
Research Center for Computational Design of Advanced Functional Materials, National Institute of Advanced Industrial Science and Technology (AIST), Central 2, 1-1-1 Umezono, Tsukuba, Ibaraki 305-8568, Japan.
J Phys Chem A. 2021 Nov 4;125(43):9518-9526. doi: 10.1021/acs.jpca.1c06685. Epub 2021 Oct 22.
Order parameters make it possible to quantify the degree of structural ordering in a material and thus to apply as the reaction coordinates during the free-energy analysis of phase or structure transitions. Furthermore, order parameters are useful in determining the local structures of molecular groups during transition stages. However, identifying or developing local order parameters (LOPs) that are sensitive for specific materials and phases is a non-trivial task. In this study, the ability of LOPs to classify the solid and liquid structures of water at coexistence or triple points is investigated with the aid of supervised machine learning. The classification accuracy of a total of 179,738,433 combinations of 493 LOPs is automatically and systematically compared for water structures at the ice Ih-Ic-liquid coexistence point and the ice III-V-liquid and ice V-VI-liquid triple points. The optimal sets of two LOPs are found for each point, and sets of three LOPs are suggested for better accuracy.
序参量使得量化材料中的结构有序程度成为可能，从而能够在相转变或结构转变的自由能分析中用作反应坐标。此外，序参量在确定转变阶段分子基团的局部结构方面很有用。然而，识别或开发对特定材料和相敏感的局部序参量（LOP）并非易事。在本研究中，借助监督机器学习研究了局部序参量对共存点或三相点处水的固体和液体结构进行分类的能力。针对冰Ih-Ic-液体共存点以及冰III-V-液体和冰V-VI-液体三相点处的水结构，自动且系统地比较了493个局部序参量的总共179,738,433种组合的分类准确率。针对每个点找到了两个局部序参量的最优集，并提出了三个局部序参量的集合以获得更高的准确率。
