Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions.
- Nguyen TT Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, California 92093, USA.
- Székely E Engineering Department, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, United Kingdom.
- Imbalzano G Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
- Behler J Universität Göttingen, Institut für Physikalische Chemie, Theoretische Chemie, Tammannstr. 6, 37077 Göttingen, Germany.
- Csányi G Engineering Department, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, United Kingdom.
- Ceriotti M Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
- Götz AW San Diego Supercomputer Center, University of California, San Diego, La Jolla, California 92093, USA.
- Paesani F Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, California 92093, USA.
- 2018-07-02
Published in:
- The Journal of chemical physics. - 2018
English
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.
- Language
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- English
- Open access status
- green
- Identifiers
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- DOI 10.1063/1.5024577
- PMID 29960316
- Persistent URL
- https://sonar.ch/global/documents/230608
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