期刊
JOURNAL OF PHYSICAL CHEMISTRY A
卷 125, 期 43, 页码 9518-9526出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jpca.1c06685
关键词
-
资金
- New Energy and Industrial Technology Development Organization (NEDO) [JPNP16010]
This study investigates the classification ability of local order parameters (LOPs) in solid and liquid structures of water using supervised machine learning, and finds optimal sets of LOPs for the different phase transition points.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据