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.



Research Center/Lab(s)

Center for High Performance Computing Research

Keywords and Phrases

Dynamics; Molecular physics; Neural networks; Potential energy surfaces; Quantum chemistry; Quantum theory; Rate constants; Many body; Network-based approach; Neural network (nn); Neuron activation function; Quantum dynamics; Science and engineering; Sum of products; Potential energy; many-body; neural network; potential energy surface; quantum dynamics; sum of products

International Standard Serial Number (ISSN)


Document Type

Article - Journal

Document Version


File Type





© 2015 Wiley Periodicals, Inc., All rights reserved.

Publication Date

01 Aug 2015