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      Structure of aqueous NaOH solutions: insights from neural-network-based molecular dynamics simulations

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          Abstract

          A reactive neural network potential is used to identify coordination polyhedra and interpolyhedron connectivity patterns of NaOH solutions.

          Abstract

          Sodium hydroxide, NaOH, is one of the most widely-used chemical reagents, but the structural properties of its aqueous solutions have only sparingly been characterized. Here, we automatically classify the cation coordination polyhedra obtained from molecular dynamics simulations. We find that, for example, with increasing concentration, octahedral coordination geometries become less favored, while the opposite is true for the trigonal prism. At high concentrations, the coordination polyhedra frequently deviate considerably from “ideal” polyhedra, because of an increased extent of interligand hydrogen-bonding, in which hydrogen bonds between two ligands, either OH 2 or OH , around the same Na + are formed. In saturated solutions, with concentrations of about 19 mol L −1, ligands are frequently shared between multiple Na + ions as a result of the deficiency of solvent molecules. This results in more complex structural patterns involving certain “characteristic” polyhedron connectivities, such as octahedra sharing ligands with capped trigonal prisms, and tetrahedra sharing ligands with trigonal bipyramids. The simulations were performed using a density-functional-theory-based reactive high-dimensional neural network potential, that was extensively validated against available neutron and X-ray diffraction data from the literature.

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          Bond-orientational order in liquids and glasses

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            Neutron scattering lengths and cross sections

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              Atom-centered symmetry functions for constructing high-dimensional neural network potentials.

              Neural networks offer an unbiased and numerically very accurate approach to represent high-dimensional ab initio potential-energy surfaces. Once constructed, neural network potentials can provide the energies and forces many orders of magnitude faster than electronic structure calculations, and thus enable molecular dynamics simulations of large systems. However, Cartesian coordinates are not a good choice to represent the atomic positions, and a transformation to symmetry functions is required. Using simple benchmark systems, the properties of several types of symmetry functions suitable for the construction of high-dimensional neural network potential-energy surfaces are discussed in detail. The symmetry functions are general and can be applied to all types of systems such as molecules, crystalline and amorphous solids, and liquids.
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                Author and article information

                Journal
                PPCPFQ
                Physical Chemistry Chemical Physics
                Phys. Chem. Chem. Phys.
                Royal Society of Chemistry (RSC)
                1463-9076
                1463-9084
                2017
                2017
                : 19
                : 1
                : 82-96
                Affiliations
                [1 ]Lehrstuhl für Theoretische Chemie
                [2 ]Ruhr-Universität Bochum
                [3 ]44780 Bochum
                [4 ]Germany
                Article
                10.1039/C6CP06547C
                c676e4ca-d1eb-4593-b013-ff1799829453
                © 2017
                History

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