JOURNAL ARTICLE

Effect of coarse graining in water models for the study of kinetics and mechanisms of clathrate hydrates nucleation and growth.

  • Published In: Journal of Chemical Physics, 2023, v. 158, n. 16. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Lauricella, Marco; Meloni, Simone; Ciccotti, Giovanni 3 of 3

Abstract

This article focuses on comparing the effects of coarse-graining in water models on the nucleation and growth of methane clathrate hydrates, specifically contrasting the coarse-grained mW model with the all-atom TIP4P force field. Using dynamical nonequilibrium molecular dynamics simulations, the study finds that while both models yield similar thermodynamic properties such as critical nucleus size and free energy curvature, their dynamical properties differ by orders of magnitude, with mW exhibiting significantly faster nucleation and growth kinetics. This difference is attributed to the absence of explicit hydrogen atoms in the mW model, which accelerates water reordering necessary for clathrate cage formation, a process slower in TIP4P due to hydrogen-bond orientation dynamics. Additionally, both models produce nuclei containing a mixture of stable structure I and less stable structure II phases, with the TIP4P model showing a stronger dependence of cage composition on methane concentration. These findings highlight the impact of water model resolution on the kinetics and structural characteristics of methane clathrate crystallization.

Additional Information

  • Source:Journal of Chemical Physics. 2023/04, Vol. 158, Issue 16, p1
  • Document Type:Article
  • Subject Area:Chemistry
  • Publication Date:2023
  • ISSN:0021-9606
  • DOI:10.1063/5.0140951
  • Accession Number:163420035
  • Copyright Statement:Copyright of Journal of Chemical Physics is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.