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Atlas of putative minima and low-lying energy networks of water clusters n = 3-25.
Rakshit, Avijit; Bandyopadhyay, Pradipta; Heindel, Joseph P; Xantheas, Sotiris S.
  • Rakshit A; School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi 110067, India.
  • Bandyopadhyay P; School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi 110067, India.
  • Heindel JP; Department of Chemistry, University of Washington, Seattle, Washington 98195, USA.
  • Xantheas SS; Department of Chemistry, University of Washington, Seattle, Washington 98195, USA.
J Chem Phys ; 151(21): 214307, 2019 Dec 07.
Article en En | MEDLINE | ID: mdl-31822087
We report a database consisting of the putative minima and ∼3.2 × 106 local minima lying within 5 kcal/mol from the putative minima for water clusters of sizes n = 3-25 using an improved version of the Monte Carlo temperature basin paving (MCTBP) global optimization procedure in conjunction with the ab initio based, flexible, polarizable Thole-Type Model (TTM2.1-F, version 2.1) interaction potential for water. Several of the low-lying structures, as well as low-lying penta-coordinated water networks obtained with the TTM2.1-F potential, were further refined at the Møller-Plesset second order perturbation (MP2)/aug-cc-pVTZ level of theory. In total, we have identified 3 138 303 networks corresponding to local minima of the clusters n = 3-25, whose Cartesian coordinates and relative energies can be obtained from the webpage https://sites.uw.edu/wdbase/. Networks containing penta-coordinated water molecules start to appear at n = 11 and, quite surprisingly, are energetically close (within 1-3 kcal/mol) to the putative minima, a fact that has been confirmed from the MP2 calculations. This large database of water cluster minima spanning quite dissimilar hydrogen bonding networks is expected to influence the development and assessment of the accuracy of interaction potentials for water as well as lower scaling electronic structure methods (such as different density functionals). Furthermore, it can also be used in conjunction with data science approaches (including but not limited to neural networks and machine and deep learning) to understand the properties of water, nature's most important substance.

Texto completo: 1 Banco de datos: MEDLINE Idioma: En Año: 2019 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Idioma: En Año: 2019 Tipo del documento: Article