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      SchNet – A deep learning architecture for molecules and materials

      1 , 2 , 1 , 3 , 1 , 4 , 5
      The Journal of Chemical Physics
      AIP Publishing

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          Generalized Gradient Approximation Made Simple

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            Is Open Access

            Quantum-chemical insights from deep tensor neural networks

            Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding quantum many-body systems? Here we develop an efficient deep learning approach that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems. We unify concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate (1 kcal mol−1) predictions in compositional and configurational chemical space for molecules of intermediate size. As an example of chemical relevance, the model reveals a classification of aromatic rings with respect to their stability. Further applications of our model for predicting atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure demonstrate the potential of machine learning for revealing insights into complex quantum-chemical systems.
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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
                The Journal of Chemical Physics
                The Journal of Chemical Physics
                AIP Publishing
                0021-9606
                1089-7690
                June 28 2018
                June 28 2018
                : 148
                : 24
                : 241722
                Affiliations
                [1 ]Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
                [2 ]Fritz-Haber-Institut der Max-Planck-Gesellschaft, 14195 Berlin, Germany
                [3 ]Physics and Materials Science Research Unit, University of Luxembourg, L-1511 Luxembourg, Luxembourg
                [4 ]Max-Planck-Institut für Informatik, Saarbrücken, Germany
                [5 ]Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul 136-713, South Korea
                Article
                10.1063/1.5019779
                29960322
                aec2c22a-7d21-45a3-9961-1e54036ea021
                © 2018
                History

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