title: Using neural networks to represent potential surfaces as sums of products.
abstract: By using exponential activation functions with a neural network (NN) method we show that it is possible to fit potentials to a sum-of-products form. The sum-of-products form is desirable because it reduces the cost of doing the quadratures required for quantum dynamics calculations. It also greatly facilitates the use of the multiconfiguration time dependent Hartree method. Unlike potfit product representation algorithm, the new NN approach does not require using a grid of points. It also produces sum-of-products potentials with fewer terms. As the number of dimensions is increased, we expect the advantages of the exponential NN idea to become more significant.
authors.affiliation: Département de chimie, Université de Montréal, Case Postale 6128, succursale Centre-ville, Montréal, (Québec) H3C 3J7, Canada. sergei.manzhos@umontreal.ca
authors.forename: Sergei
authors.initials: S
authors.lastname: Manzhos
authors.forename: Tucker
authors.initials: T
authors.lastname: Carrington
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D006861:Hydrogen Peroxide / Q000737:chemistry*; D016571:Neural Networks, Computer; D009474:Neurons / Q000737:chemistry*
chemical_list: D006861:Hydrogen Peroxide