4.7 Article

Atlas of putative minima and low-lying energy networks of water clusters n=3-25

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JOURNAL OF CHEMICAL PHYSICS
卷 151, 期 21, 页码 -

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AMER INST PHYSICS
DOI: 10.1063/1.5128378

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  1. U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences
  2. Office of Science of the U.S. Department of Energy [DE-AC02-05CH11231]
  3. DST-PURSE
  4. JNU
  5. CSIR SRF fellowship [9/263(1116)/2017 EMR-I]
  6. UPE-II

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We report a database consisting of the putative minima and similar to 3.2 x 10(6) 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 Moller-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. Published under license by AIP Publishing.

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