Blog
About

0
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Machine Learning Approaches toward Orbital-free Density Functional Theory: Simultaneous Training on the Kinetic Energy Density Functional and Its Functional Derivative

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Orbital-free approaches might offer a way to boost the applicability of density functional theory by orders of magnitude in system size. An important ingredient for this endeavor is the kinetic energy density functional. Snyder et al. [Phys. Rev. Lett.2012, 108, 253002[ PubMed]] presented a machine learning approximation for this functional achieving chemical accuracy on a one-dimensional model system. However, a poor performance with respect to the functional derivative, a crucial element in iterative energy minimization procedures, enforced the application of a computationally expensive projection method. In this work we circumvent this issue by including the functional derivative into the training of various machine learning models. Besides kernel ridge regression, the original method of choice, we also test the performance of convolutional neural network techniques borrowed from the field of image recognition.

          Related collections

          Most cited references 53

          • Record: found
          • Abstract: not found
          • Article: not found

          A tutorial on support vector regression

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Zur Theorie der Kernmassen

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              The calculation of atomic fields

               L H Thomas (1927)
                Bookmark

                Author and article information

                Journal
                J Chem Theory Comput
                J Chem Theory Comput
                ct
                jctcce
                Journal of Chemical Theory and Computation
                American Chemical Society
                1549-9618
                1549-9626
                10 August 2020
                08 September 2020
                : 16
                : 9
                : 5685-5694
                Affiliations
                Institute of Experimental Physics, Graz University of Technology , Petersgasse 16, 8010 Graz, Austria
                Author notes
                Article
                10.1021/acs.jctc.0c00580
                7482319
                Copyright © 2020 American Chemical Society

                This is an open access article published under a Creative Commons Attribution (CC-BY) License, which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited.

                Categories
                Article
                Custom metadata
                ct0c00580
                ct0c00580

                Computational chemistry & Modeling

                Comments

                Comment on this article