Neural Network-based Approaches for Building High Dimensional and Quantum Dynamics-friendly Potential Energy Surfaces
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. © 2014 Wiley Periodicals, Inc.
S. Manzhos et al., "Neural Network-based Approaches for Building High Dimensional and Quantum Dynamics-friendly Potential Energy Surfaces," International Journal of Quantum Chemistry, vol. 115, no. 16, SI, pp. 1012-1020, Wiley Periodicals, Inc, Jan 2015.
The definitive version is available at http://dx.doi.org/10.1002/qua.24795
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