A neural network potential-energy surface for the water dimer based on environment-dependent atomic energies and charges

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

(2012) Nongnuch Artrith et al.   PHYSICAL REVIEW B

Atom-centered symmetry functions for constructing high-dimensional neural network potentials

(2011) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Nucleation mechanism for the direct graphite-to-diamond phase transition

(2011) Rustam Z. Khaliullin et al.   NATURE MATERIALS

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

(2011) Jörg Behler   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide

(2011) Nongnuch Artrith et al.   PHYSICAL REVIEW B

Towards an ab initio flexible potential for water, and post-harmonic quantum vibrational analysis of water clusters

(2010) Yimin Wang et al.   CHEMICAL PHYSICS LETTERS

A reactive molecular dynamics simulation of the silica-water interface

(2010) Joseph C. Fogarty et al.   JOURNAL OF CHEMICAL PHYSICS

Potential Energy Surfaces Fitted by Artificial Neural Networks

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

Ab initioquality neural-network potential for sodium

(2010) Hagai Eshet et al.   PHYSICAL REVIEW B

Graphite-diamond phase coexistence study employing a neural-network mapping of theab initiopotential energy surface

(2010) Rustam Z. Khaliullin et al.   PHYSICAL REVIEW B

Signatures of nonadiabaticO2dissociation at Al(111): First-principles fewest-switches study

(2010) Christian Carbogno et al.   PHYSICAL REVIEW B

Towards the complete understanding of water by a first-principles computational approach

(2009) Krzysztof Szalewicz et al.   CHEMICAL PHYSICS LETTERS

Ab initio molecular simulations with numeric atom-centered orbitals

(2009) Volker Blum et al.   COMPUTER PHYSICS COMMUNICATIONS

Simultaneous fitting of a potential-energy surface and its corresponding force fields using feedforward neural networks

(2009) A. Pukrittayakamee et al.   JOURNAL OF CHEMICAL PHYSICS

Dynamically Polarizable Water Potential Based on Multipole Moments Trained by Machine Learning

(2009) Chris M. Handley et al.   Journal of Chemical Theory and Computation

Molecular Dynamics Investigation of the Bimolecular Reaction BeH + H2→ BeH2+ H on an ab Initio Potential-Energy Surface Obtained Using Neural Network Methods with Both Potential and Gradient Accuracy Determination

(2009) Hung M. Le et al.   JOURNAL OF PHYSICAL CHEMISTRY A

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

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

Polarizable interaction potential for water from coupled cluster calculations. I. Analysis of dimer potential energy surface

(2008) Robert Bukowski et al.   JOURNAL OF CHEMICAL PHYSICS

Mapping Potential Energy Surfaces by Neural Networks: The ethanol/Au(111) interface

(2008) Diogo A.R.S. Latino et al.   JOURNAL OF ELECTROANALYTICAL CHEMISTRY

Silicon potentials investigated using density functional theory fitted neural networks

(2008) E Sanville et al.   JOURNAL OF PHYSICS-CONDENSED MATTER

Pressure-induced phase transitions in silicon studied by neural network-based metadynamics simulations

(2008) Jörg Behler et al.   PHYSICA STATUS SOLIDI B-BASIC SOLID STATE PHYSICS

Nonadiabatic effects in the dissociation of oxygen molecules at the Al(111) surface

(2008) Jörg Behler et al.   PHYSICAL REVIEW B

Fingerprints for Spin-Selection Rules in the Interaction Dynamics ofO2at Al(111)

(2008) Christian Carbogno et al.   PHYSICAL REVIEW LETTERS

Metadynamics Simulations of the High-Pressure Phases of Silicon Employing a High-Dimensional Neural Network Potential

(2008) Jörg Behler et al.   PHYSICAL REVIEW LETTERS