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      Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions

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          Studies in Molecular Dynamics. I. General Method

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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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              A simple and efficient CCSD(T)-F12 approximation.

              A new explicitly correlated CCSD(T)-F12 approximation is presented and tested for 23 molecules and 15 chemical reactions. The F12 correction strongly improves the basis set convergence of correlation and reaction energies. Errors of the Hartree-Fock contributions are effectively removed by including MP2 single excitations into the auxiliary basis set. Using aug-cc-pVTZ basis sets the CCSD(T)-F12 calculations are more accurate and two orders of magnitude faster than standard CCSD(T)/aug-cc-pV5Z calculations.
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                Author and article information

                Journal
                The Journal of Chemical Physics
                The Journal of Chemical Physics
                AIP Publishing
                0021-9606
                1089-7690
                June 28 2018
                June 28 2018
                : 148
                : 24
                : 241725
                Affiliations
                [1 ]Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, California 92093, USA
                [2 ]San Diego Supercomputer Center, University of California, San Diego, La Jolla, California 92093, USA
                [3 ]Engineering Department, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, United Kingdom
                [4 ]Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
                [5 ]Universität Göttingen, Institut für Physikalische Chemie, Theoretische Chemie, Tammannstr. 6, 37077 Göttingen, Germany
                Article
                10.1063/1.5024577
                29960316
                3851f54e-48c8-4858-a20d-862b54662213
                © 2018

                https://publishing.aip.org/authors/rights-and-permissions

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