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      Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures

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          Efficient iterative schemes forab initiototal-energy calculations using a plane-wave basis set

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            Perspective: Machine learning potentials for atomistic simulations

            Nowadays, computer simulations have become a standard tool in essentially all fields of chemistry, condensed matter physics, and materials science. In order to keep up with state-of-the-art experiments and the ever growing complexity of the investigated problems, there is a constantly increasing need for simulations of more realistic, i.e., larger, model systems with improved accuracy. In many cases, the availability of sufficiently efficient interatomic potentials providing reliable energies and forces has become a serious bottleneck for performing these simulations. To address this problem, currently a paradigm change is taking place in the development of interatomic potentials. Since the early days of computer simulations simplified potentials have been derived using physical approximations whenever the direct application of electronic structure methods has been too demanding. Recent advances in machine learning (ML) now offer an alternative approach for the representation of potential-energy surfaces by fitting large data sets from electronic structure calculations. In this perspective, the central ideas underlying these ML potentials, solved problems and remaining challenges are reviewed along with a discussion of their current applicability and limitations.
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              Lithium Storage in Carbon Nanostructures

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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
                : 241714
                Affiliations
                [1 ]Engineering Laboratory, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
                [2 ]École des Ponts ParisTech, F-77455 Marne-la-Vallée Cedex 2, France
                [3 ]Department of Materials Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
                [4 ]Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
                Article
                10.1063/1.5016317
                51311e5d-9fb1-4d01-97f3-1ea1dcaa4bf1
                © 2018
                History

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