Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions.
Nguyen, Thuong T
Götz, Andreas W
J Chem Phys
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Nguyen, T. T., Székely, E., Imbalzano, G., Behler, J., Csányi, G., Ceriotti, M., Götz, A. W., & et al. (2018). Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions.. J Chem Phys, 148 (24), 241725-241725. https://doi.org/10.1063/1.5024577
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.
This work was supported by the National Science Foundation through Grant No. ACI-1642336 (to F.P. and A.W.G.). This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation Grant No. ACI-1548562. J.B. is grateful for a Heisenberg professorship funded by the DFG (No. Be3264/11-2). E.Sz. would like to acknowledge the support of the Peterhouse Research Studentship and the support of BP International Centre for Advanced Materials (ICAM). M.C. was supported by the European Research Council under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 677013-HBMAP). G.I. acknowledges funding from the Fondazione Zegna
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External DOI: https://doi.org/10.1063/1.5024577
This record's URL: https://www.repository.cam.ac.uk/handle/1810/278996