Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions

DOI PDF Web Site 被引用文献2件
  • Thuong T. Nguyen
    Department of Chemistry and Biochemistry, University of California, San Diego 1 , La Jolla, California 92093, USA
  • Eszter Székely
    Engineering Department, University of Cambridge 3 , Trumpington Street, Cambridge CB2 1PZ, United Kingdom
  • Giulio Imbalzano
    Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne 4 , 1015 Lausanne, Switzerland
  • Jörg Behler
    Universität Göttingen, Institut für Physikalische Chemie, Theoretische Chemie 5 , Tammannstr. 6, 37077 Göttingen, Germany
  • Gábor Csányi
    Engineering Department, University of Cambridge 3 , Trumpington Street, Cambridge CB2 1PZ, United Kingdom
  • Michele Ceriotti
    Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne 4 , 1015 Lausanne, Switzerland
  • Andreas W. Götz
    San Diego Supercomputer Center, University of California, San Diego 2 , La Jolla, California 92093, USA
  • Francesco Paesani
    Department of Chemistry and Biochemistry, University of California, San Diego 1 , La Jolla, California 92093, USA

書誌事項

公開日
2018-04-09
DOI
  • 10.1063/1.5024577
公開者
AIP Publishing

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<jats:p>The accurate representation of multidimensional potential energy surfaces is a necessary requirement for realistic computer simulations of molecular systems. The continued increase in computer power accompanied by advances in correlated electronic structure methods nowadays enables routine calculations of accurate interaction energies for small systems, which can then be used as references for the development of analytical potential energy functions (PEFs) rigorously derived from many-body (MB) expansions. Building on the accuracy of the MB-pol many-body PEF, we investigate here the performance of permutationally invariant polynomials (PIPs), neural networks, and Gaussian approximation potentials (GAPs) in representing water two-body and three-body interaction energies, denoting the resulting potentials PIP-MB-pol, Behler-Parrinello neural network-MB-pol, and GAP-MB-pol, respectively. Our analysis shows that all three analytical representations exhibit similar levels of accuracy in reproducing both two-body and three-body reference data as well as interaction energies of small water clusters obtained from calculations carried out at the coupled cluster level of theory, the current gold standard for chemical accuracy. These results demonstrate the synergy between interatomic potentials formulated in terms of a many-body expansion, such as MB-pol, that are physically sound and transferable, and machine-learning techniques that provide a flexible framework to approximate the short-range interaction energy terms.</jats:p>

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