Journal
INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
Volume 115, Issue 16, Pages 1012-1020Publisher
WILEY
DOI: 10.1002/qua.24795
Keywords
potential energy surface; neural network; quantum dynamics; sum of products; many-body
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Funding
- Natural Sciences and Engineering Research Council of Canada (NSERC)
- National Science Foundation [CHE-1300945]
- Ministry of Education of Singapore [AcRF Grant] [WBS R-265-000-494-112]
- Direct For Mathematical & Physical Scien
- Division Of Chemistry [1300945] Funding Source: National Science Foundation
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Development and applications of neural network (NN)-based approaches for representing potential energy surfaces (PES) of bound and reactive molecular systems are reviewed. Specifically, it is shown that when the density of ab initio points is low, NNs-based potentials with multibody or multimode structure are advantageous for representing high-dimensional PESs. Importantly, with an appropriate choice of the neuron activation function, PESs in the sum-of-products form are naturally obtained, thus addressing a bottleneck problem in quantum dynamics. The use of NN committees is also analyzed and it is shown that while they are able to reduce the fitting error, the reduction is limited by the nonrandom nature of the fitting error. The approaches described here are expected to be directly applicable in other areas of science and engineering where a functional form needs to be constructed in an unbiased way from sparse data. (c) 2014 Wiley Periodicals, Inc.
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