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Two Local States of Ambient Water

Author(s)
Lee, YongjinLee, YoungKyuJeong, SeongMinKumar, AnupamJho, YongSeok
Issued Date
2020-01
DOI
10.3938/jkps.76.1
URI
https://scholarworks.unist.ac.kr/handle/201301/31318
Fulltext
https://link.springer.com/article/10.3938%2Fjkps.76.1
Citation
JOURNAL OF THE KOREAN PHYSICAL SOCIETY, v.76, no.1, pp.1 - 7
Abstract
The non-monotonic trends of thermodynamic response functions have long been a mystery of water. The idea, that water may be a mixture of two local states, came out more than a century ago to explain the origin of the non-monotonic behaviors. Recently, this idea is materialized through the hypothesis of the second critical point of water and then the anomalies are outcomes of critical fluctuation. Although the typical macroscopic heterogeneity (Widom line) of critical fluctuation stays in the vicinity of the critical point, as we have previously shown that the microscopic heterogeneity is identified far from it which extends the linear heterogeneity, the Widom line, to the areal one as a Widom Delta. With this background, we search for two local states of the ambient water. Distinct states in ambient condition are not to be contrasted by a single strong feature such as density but they are expressed by a combination of weak features that reflects locally correlated structures. In this work, we identify the formation of local bicontinuous micro-domain formations of water attributing its softness by using machine learning order parameters. Interestingly, the radial distribution functions are similar to two phases in the liquid-liquid phase transition and they are well fitted by the two-state model. The hard-label domain is dominant at a lower temperature but changes its label to a more fluctuating soft-label domain at high temperature. There exist crossover behaviors around 310-320 K. At sufficiently high temperatures, near the liquid-gas phase transition, all water molecules become homogeneous.
Publisher
KOREAN PHYSICAL SOC
ISSN
0374-4884
Keyword (Author)
Water AnomalyLiquid-Liquid Phase SeparationWidom DeltaMachine Learning
Keyword
BOND NETWORKDENSITYMODEL

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