First-principles interatomic potentials for ten elemental metals via compressed sensing

Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties

(2015) O. Anatole von Lilienfeld et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

A compact and accurate semi-global potential energy surface for malonaldehyde from constrained least squares regression

(2014) Wataru Mizukami et al.   JOURNAL OF CHEMICAL PHYSICS

Permutation invariant polynomial neural network approach to fitting potential energy surfaces. III. Molecule-surface interactions

(2014) Bin Jiang et al.   JOURNAL OF CHEMICAL PHYSICS

Representing potential energy surfaces by high-dimensional neural network potentials

(2014) J Behler   JOURNAL OF PHYSICS-CONDENSED MATTER

Sparse representation for a potential energy surface

(2014) Atsuto Seko et al.   PHYSICAL REVIEW B

How to represent crystal structures for machine learning: Towards fast prediction of electronic properties

(2014) K. T. Schütt et al.   PHYSICAL REVIEW B

Lattice Anharmonicity and Thermal Conductivity from Compressive Sensing of First-Principles Calculations

(2014) Fei Zhou et al.   PHYSICAL REVIEW LETTERS

Automated fit of high-dimensional potential energy surfaces using cluster analysis and interpolation over descriptors of chemical environment

(2013) René Fournier 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

Compressive sensing as a paradigm for building physics models

(2013) Lance J. Nelson et al.   PHYSICAL REVIEW B

On representing chemical environments

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

Construction of high-dimensional neural network potentials using environment-dependent atom pairs

(2012) K. V. Jovan Jose et al.   JOURNAL OF CHEMICAL PHYSICS

Modified Feed-Forward Neural Network Structures and Combined-Function-Derivative Approximations Incorporating Exchange Symmetry for Potential Energy Surface Fitting

(2012) Hieu T. T. Nguyen et al.   JOURNAL OF PHYSICAL CHEMISTRY A

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

(2012) Nongnuch Artrith et al.   PHYSICAL REVIEW B

Neural network interatomic potential for the phase change material GeTe

(2012) Gabriele C. Sosso et al.   PHYSICAL REVIEW B

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

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

(2011) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

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

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

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

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

Accurate prediction of higher-level electronic structure energies for large databases using neural networks, Hartree–Fock energies, and small subsets of the database

(2009) M. Malshe et al.   JOURNAL OF CHEMICAL PHYSICS

Molecular dissociation of hydrogen peroxide (HOOH) on a neural network ab initio potential surface with a new configuration sampling method involving gradient fitting

(2009) Hung M. Le et al.   JOURNAL OF CHEMICAL PHYSICS

A Self-Starting Method for Obtaining Analytic Potential-Energy Surfaces from ab Initio Electronic Structure Calculations

(2009) P. M. Agrawal et al.   JOURNAL OF PHYSICAL CHEMISTRY A

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

Interpolating moving least-squares methods for fitting potential energy surfaces: A strategy for efficient automatic data point placement in high dimensions

(2008) Richard Dawes et al.   JOURNAL OF CHEMICAL PHYSICS

Beyond Point Charges: Dynamic Polarization from Neural Net Predicted Multipole Moments

(2008) Michael G. Darley et al.   Journal of Chemical Theory and Computation

First-principles calculations of the ferroelastic transition between rutile-type andCaCl2-typeSiO2at high pressures

(2008) Atsushi Togo et al.   PHYSICAL REVIEW B