Neural networks vs Gaussian process regression for representing potential energy surfaces: A comparative study of fit quality and vibrational spectrum accuracy

Perspective: Computing (ro-)vibrational spectra of molecules with more than four atoms

(2017) Tucker Carrington   JOURNAL OF CHEMICAL PHYSICS

A ground state potential energy surface for HONO based on a neural network with exponential fitting functions

(2017) Ekadashi Pradhan et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Efficient and accurate evaluation of potential energy matrix elements for quantum dynamics using Gaussian process regression

(2016) Jonathan P. Alborzpour et al.   JOURNAL OF CHEMICAL PHYSICS

Using an internal coordinate Gaussian basis and a space-fixed Cartesian coordinate kinetic energy operator to compute a vibrational spectrum with rectangular collocation

(2016) Sergei Manzhos et al.   JOURNAL OF CHEMICAL PHYSICS

Efficient non-parametric fitting of potential energy surfaces for polyatomic molecules with Gaussian processes

(2016) Jie Cui et al.   JOURNAL OF PHYSICS B-ATOMIC MOLECULAR AND OPTICAL PHYSICS

Gaussian process model for extrapolation of scattering observables for complex molecules: From benzene to benzonitrile

(2015) Jie Cui et al.   JOURNAL OF CHEMICAL PHYSICS

Explicitly correlated MRCI-F12 potential energy surfaces for methane fit with several permutation invariant schemes and full-dimensional vibrational calculations

(2015) Moumita Majumder et al.   MOLECULAR PHYSICS

Gaussian Process Model for Collision Dynamics of Complex Molecules

(2015) Jie Cui et al.   PHYSICAL REVIEW LETTERS

Neural network-based approaches for building high dimensional and quantum dynamics-friendly potential energy surfaces

(2014) Sergei Manzhos et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Calculating vibrational spectra with sum of product basis functions without storing full-dimensional vectors or matrices

(2014) Arnaud Leclerc et al.   JOURNAL OF CHEMICAL PHYSICS

Computing rovibrational levels of methane with curvilinear internal vibrational coordinates and an Eckart frame

(2013) Xiao-Gang Wang et al.   JOURNAL OF CHEMICAL PHYSICS

Machine-learning approach for one- and two-body corrections to density functional theory: Applications to molecular and condensed water

(2013) Albert P. Bartók et al.   PHYSICAL REVIEW B

Using nonproduct quadrature grids to solve the vibrational Schrödinger equation in 12D

(2011) Gustavo Avila et al.   JOURNAL OF CHEMICAL PHYSICS

Three-body interactions in clusters CO–(pH2)n

(2010) Sergei Manzhos et al.   CHEMICAL PHYSICS LETTERS

Potential Energy Surfaces Fitted by Artificial Neural Networks

(2010) Chris M. Handley et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

(2010) Albert P. Bartók et al.   PHYSICAL REVIEW LETTERS

Fitting sparse multidimensional data with low-dimensional terms

(2009) Sergei Manzhos et al.   COMPUTER PHYSICS COMMUNICATIONS

Permutationally invariant potential energy surfaces in high dimensionality

(2009) Bastiaan J. Braams et al.   INTERNATIONAL REVIEWS IN PHYSICAL CHEMISTRY

Optimal construction of a fast and accurate polarisable water potential based on multipole moments trained by machine learning

(2009) Chris M. Handley et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

A model for the dissociative adsorption of N2O on Cu(100) using a continuous potential energy surface

(2009) Sergei Manzhos et al.   SURFACE SCIENCE

Using neural networks, optimized coordinates, and high-dimensional model representations to obtain a vinyl bromide potential surface

(2008) Sergei Manzhos et al.   JOURNAL OF CHEMICAL PHYSICS