Construction of reactive potential energy surfaces with Gaussian process regression: active data selection

Toolkit for the Construction of Reproducing Kernel-Based Representations of Data: Application to Multidimensional Potential Energy Surfaces

(2017) Oliver T. Unke et al.   Journal of Chemical Information and Modeling

High-Dimensional Atomistic Neural Network Potentials for Molecule–Surface Interactions: HCl Scattering from Au(111)

(2017) Brian Kolb et al.   Journal of Physical Chemistry Letters

Recent Advances in Quantum Dynamics of Bimolecular Reactions

(2016) Dong H. Zhang et al.   Annual Review of Physical Chemistry

Potential energy surfaces from high fidelity fitting ofab initiopoints: the permutation invariant polynomial - neural network approach

(2016) Bin Jiang et al.   INTERNATIONAL REVIEWS IN PHYSICAL CHEMISTRY

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

A seven-dimensional quantum dynamics study of the dissociative chemisorption of H2O on Cu(111): effects of azimuthal angles and azimuthal angle-averaging

(2016) Tianhui Liu et al.   Chemical Science

First-principles quantum dynamical theory for the dissociative chemisorption of H2O on rigid Cu(111)

(2016) Zhaojun Zhang et al.   Nature Communications

Methane dissociation on Ni(111): A fifteen-dimensional potential energy surface using neural network method

(2015) Xiangjian Shen et al.   JOURNAL OF CHEMICAL PHYSICS

Gaussian Process Model for Collision Dynamics of Complex Molecules

(2015) Jie Cui et al.   PHYSICAL REVIEW LETTERS

Global Potential Energy Surface for the H+CH4↔H2+CH3 Reaction using Neural Networks

(2014) Xin Xu et al.   CHINESE JOURNAL OF CHEMICAL PHYSICS

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

A global potential energy surface for the H2 + OH ↔ H2O + H reaction using neural networks

(2013) Jun Chen et al.   JOURNAL OF CHEMICAL PHYSICS

Permutation invariant polynomial neural network approach to fitting potential energy surfaces. II. Four-atom systems

(2013) Jun Li et al.   JOURNAL OF CHEMICAL PHYSICS

Permutation invariant polynomial neural network approach to fitting potential energy surfaces

(2013) Bin Jiang et al.   JOURNAL OF CHEMICAL PHYSICS

Efficient Gaussian process regression for large datasets

(2012) A. Banerjee et al.   BIOMETRIKA

High-dimensional neural network potentials for metal surfaces: A prototype study for copper

(2012) Nongnuch Artrith et al.   PHYSICAL REVIEW B

High-dimensional ab initio potential energy surfaces for reaction dynamics calculations

(2011) Joel M. Bowman et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations

(2011) Jörg Behler   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Potential Energy Surfaces Fitted by Artificial Neural Networks

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

Permutationally invariant potential energy surfaces in high dimensionality

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

Interpolating moving least-squares methods for fitting potential energy surfaces: Using classical trajectories to explore configuration space

(2009) Richard Dawes et al.   JOURNAL OF 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