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      Machine-learning the configurational energy of multicomponent crystalline solids

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      npj Computational Materials
      Springer Nature

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          Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)

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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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              Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

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                Author and article information

                Journal
                npj Computational Materials
                npj Comput Mater
                Springer Nature
                2057-3960
                December 2018
                November 1 2018
                December 2018
                : 4
                : 1
                Article
                10.1038/s41524-018-0110-y
                dbcef804-f4d2-481d-bae9-b44ce3f0df7e
                © 2018

                http://creativecommons.org/licenses/by/4.0

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