4.8 Article

Machine learning of accurate energy-conserving molecular force fields

Related references

Note: Only part of the references are listed.
Article Chemistry, Physical

Perspective: Machine learning potentials for atomistic simulations

Joerg Behler

JOURNAL OF CHEMICAL PHYSICS (2016)

Article Chemistry, Physical

Comparing molecules and solids across structural and alchemical space

Sandip De et al.

PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2016)

Article Chemistry, Multidisciplinary

Modeling quantum nuclei with perturbed path integral molecular dynamics

Igor Poltavsky et al.

CHEMICAL SCIENCE (2016)

Article Chemistry, Physical

Nonlinear gradient denoising: Finding accurate extrema from inaccurate functional derivatives

John C. Snyder et al.

INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2015)

Review Chemistry, Physical

Gaussian approximation potentials: A brief tutorial introduction

Albert P. Bartok et al.

INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2015)

Article Chemistry, Physical

Machine Learning for Quantum Mechanical Properties of Atoms in Molecules

Matthias Rupp et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2015)

Article Chemistry, Physical

Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

Katja Hansen et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2015)

Article Materials Science, Multidisciplinary

Learning scheme to predict atomic forces and accelerate materials simulations

V. Botu et al.

PHYSICAL REVIEW B (2015)

Article Physics, Multidisciplinary

Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces

Zhenwei Li et al.

PHYSICAL REVIEW LETTERS (2015)

Article Computer Science, Interdisciplinary Applications

i-PI: A Python interface for ab initio path integral molecular dynamics simulations

Michele Ceriotti et al.

COMPUTER PHYSICS COMMUNICATIONS (2014)

Article Chemistry, Physical

Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies

Katja Hansen et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2013)

Article Physics, Multidisciplinary

Machine learning of molecular electronic properties in chemical compound space

Gregoire Montavon et al.

NEW JOURNAL OF PHYSICS (2013)

Article Materials Science, Multidisciplinary

On representing chemical environments

Albert P. Bartok et al.

PHYSICAL REVIEW B (2013)

Article Chemistry, Physical

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

K. V. Jovan Jose et al.

JOURNAL OF CHEMICAL PHYSICS (2012)

Article Physics, Multidisciplinary

Finding Density Functionals with Machine Learning

John C. Snyder et al.

PHYSICAL REVIEW LETTERS (2012)

Article Physics, Multidisciplinary

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

Matthias Rupp et al.

PHYSICAL REVIEW LETTERS (2012)

Article Chemistry, Physical

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

Joerg Behler

JOURNAL OF CHEMICAL PHYSICS (2011)

Article Chemistry, Physical

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

Joerg Behler

PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2011)

Article Statistics & Probability

Matern Cross-Covariance Functions for Multivariate Random Fields

Tilmann Gneiting et al.

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (2010)

Article Physics, Multidisciplinary

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

Albert P. Bartok et al.

PHYSICAL REVIEW LETTERS (2010)

Article Physics, Multidisciplinary

Accurate Molecular Van Der Waals Interactions from Ground-State Electron Density and Free-Atom Reference Data

Alexandre Tkatchenko et al.

PHYSICAL REVIEW LETTERS (2009)

Article Chemistry, Physical

Representing molecule-surface interactions with symmetry-adapted neural networks

Jorg Behler et al.

JOURNAL OF CHEMICAL PHYSICS (2007)

Article Physics, Multidisciplinary

Generalized neural-network representation of high-dimensional potential-energy surfaces

Joerg Behler et al.

PHYSICAL REVIEW LETTERS (2007)

Article Computer Science, Artificial Intelligence

On learning vector-valued functions

CA Micchelli et al.

NEURAL COMPUTATION (2005)

Review Computer Science, Artificial Intelligence

An introduction to kernel-based learning algorithms

KR Müller et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS (2001)