Analysis of the anomalous mean-field like properties of Gaussian core model in terms of entropy

TitleAnalysis of the anomalous mean-field like properties of Gaussian core model in terms of entropy
Publication TypeJournal Article
Year of Publication2018
AuthorsNandi, MKumar, Bhattacharyya, SMaitra
JournalJournal of Chemical Physics
Volume148
Issue3
Pagination034504
Date PublishedJAN
Type of ArticleArticle
ISSN0021-9606
Abstract

Studies of the Gaussian core model (GCM) have shown that it behaves like a mean-field model and the properties are quite different from standard glass former. In this work, we investigate the entropies, namely, the excess entropy (Sex) and the configurational entropy (S-c) and their different components to address these anomalies. Our study corroborates most of the earlier observations and also sheds new light on the high and low temperature dynamics. We find that unlike in standard glass former where high temperature dynamics is dominated by two-body correlation and low temperature by many-body correlations, in the GCM both high and low temperature dynamics are dominated by many-body correlations. We also find that the many-body entropy which is usually positive at low temperatures and is associated with activated dynamics is negative in the GCM suggesting suppression of activation. Interestingly despite the suppression of activation, the Adam-Gibbs (AG) relation that describes activated dynamics holds in the GCM, thus suggesting a non-activated contribution in AG relation. We also find an overlap between the AG relation and mode coupling power law regime leading to a power law behavior of S-c. From our analysis of this power law behavior, we predict that in the GCM the high temperature dynamics will disappear at dynamical transition temperature and below that there will be a transition to the activated regime. Our study further reveals that the activated regime in the GCM is quite narrow. Published by AIP Publishing.

DOI10.1063/1.5013644
Type of Journal (Indian or Foreign)Foreign
Impact Factor (IF)2.965
Divison category: 
Polymer Science & Engineering

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