The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Permutation invariant polynomial neural network approach to fitting potential energy surfaces
Abstract
A simple, general, and rigorous scheme for adapting permutation symmetry in molecular systems is proposed and tested for fitting global potential energy surfaces using neural networks (NNs). The symmetry adaptation is realized by using low-order permutation invariant polynomials (PIPs) as inputs for the NNs. This so-called PIP-NN approach is applied to the H + H2 and Cl + H2 systems and the analytical potential energy surfaces for these two systems were accurately reproduced by PIP-NN. The accuracy of the NN potential energy surfaces was confirmed by quantum scattering calculations.
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Citations
Cites
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
Behler, J., & Parrinello, M. (2007) · Physical Review Letters · doi:10.1103/physrevlett.98.146401 · Cited by 5,010
On the distribution of points in a cube and the approximate evaluation of integrals
Sobol', I. M. (1967) · USSR Computational Mathematics and Mathematical Physics · doi:10.1016/0041-5553(67)90144-9 · Cited by 2,975
Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations
Behler, J. (2011) · Physical Chemistry Chemical Physics · doi:10.1039/c1cp21668f · Cited by 781
Cited by
Four Generations of High-Dimensional Neural Network Potentials
Behler, J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00868 · Cited by 873
Physics-Inspired Structural Representations for Molecules and Materials
Musil, F., Grisafi, A., Bartók, A. P., Ortner, C., Csányi, G., & Ceriotti, M. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00021 · Cited by 523
Jiang, B., Li, J., & Guo, H. (2016) · International Reviews in Physical Chemistry · doi:10.1080/0144235x.2016.1200347 · Cited by 401
Self-citationCommunication: Fitting potential energy surfaces with fundamental invariant neural network
Shao, K., Chen, J., Zhao, Z., & Zhang, D. H. (2016) · The Journal of Chemical Physics · doi:10.1063/1.4961454 · Cited by 258
Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network
Yao, K., Herr, J. E., Brown, S. N., & Parkhill, J. (2017) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.7b01072 · Cited by 126
Metadata
- Authors
- Jiang, B., & Guo, H.
- Venue
- The Journal of Chemical Physics
- Publisher
- AIP Publishing
- Published
- 2013-08-06
- Volume
- 139
- Issue
- 5
- Pages
- 054112-054112
- ISSN
- 0021-9606 (ISSN-L)
- 1089-7690
- OpenAlex
- W2068473123
- Cited by
- 541
- References
- 3
- FWCI
- 14.74
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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