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      Unsupervised machine learning for detection of phase transitions in off-lattice systems. II. Applications

      Author(s): 1 , 1 , 2 , 1 , 1 , 3
      Publication date Created:
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      Journal: The Journal of Chemical Physics
      Publisher: AIP Publishing

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          SchNet – A deep learning architecture for molecules and materials

          K.-R. Muller, H. E. Sauceda, P-J Kindermans … (2018)
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            General purpose molecular dynamics simulations fully implemented on graphics processing units

            Joshua A. Anderson, Chris Lorenz, A. Travesset (2008)
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              Learning phase transitions by confusion

              Evert van Nieuwenburg, Ye-Hua Liu, Sebastian Huber (2017)
              A neural-network technique can exploit the power of machine learning to mine the exponentially large data sets characterizing the state space of condensed-matter systems. Topological transitions and many-body localization are first on the list.
              Article 0 comments Cited 258 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Title: The Journal of Chemical Physics
                Abbreviated Title: J. Chem. Phys.
                Publisher: AIP Publishing
                ISSN (Print): 0021-9606
                ISSN (Electronic): 1089-7690
                Publication date Created: November 21 2018
                Publication date (Print): November 21 2018
                Volume: 149
                Issue: 19
                Page: 194110
                Affiliations
                [1 ]McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, USA
                [2 ]Department of Chemistry, University of Texas at Austin, Austin, Texas 78712, USA
                [3 ]Department of Physics, University of Texas at Austin, Austin, Texas 78712, USA
                Article
                DOI: 10.1063/1.5049850
                SO-VID: 30f31095-fcd7-484c-9bf1-251edb962090
                Copyright © © 2018
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