A novel approach to describe chemical environments in high-dimensional neural network potentials

Achieving DFT accuracy with a machine-learning interatomic potential: Thermomechanics and defects in bcc ferromagnetic iron

(2018) Daniele Dragoni et al.   PHYSICAL REVIEW MATERIALS

Machine learning prediction of interaction energies in rigid water clusters

(2018) Samik Bose et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

(2017) J. S. Smith et al.   Chemical Science

Discovering the building blocks of atomic systems using machine learning: application to grain boundaries

(2017) Conrad W. Rosenbrock et al.   npj Computational Materials

Perspective: Machine learning potentials for atomistic simulations

(2016) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Machine Learning Force Fields: Construction, Validation, and Outlook

(2016) V. Botu et al.   Journal of Physical Chemistry C

Comparing molecules and solids across structural and alchemical space

(2016) Sandip De et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Neural network molecular dynamics simulations of solid–liquid interfaces: water at low-index copper surfaces

(2016) Suresh Kondati Natarajan et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

How van der Waals interactions determine the unique properties of water

(2016) Tobias Morawietz et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Deep learning

(2015) Yann LeCun et al.   NATURE

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

(2015) Zhenwei Li et al.   PHYSICAL REVIEW LETTERS

Accuracy and transferability of Gaussian approximation potential models for tungsten

(2014) Wojciech J. Szlachta et al.   PHYSICAL REVIEW B

On representing chemical environments

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

The classical molecular dynamics simulation of graphene on Ru(0001) using a fitted Tersoff interface potential

(2013) P. Süle et al.   SURFACE AND INTERFACE ANALYSIS

Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups

(2012) Geoffrey Hinton et al.   IEEE SIGNAL PROCESSING MAGAZINE

Perspective on density functional theory

(2012) Kieron Burke   JOURNAL OF CHEMICAL PHYSICS

From the computer to the laboratory: materials discovery and design using first-principles calculations

(2012) Geoffroy Hautier et al.   JOURNAL OF MATERIALS SCIENCE

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

(2012) Nongnuch Artrith et al.   PHYSICAL REVIEW B

Finding Density Functionals with Machine Learning

(2012) John C. Snyder et al.   PHYSICAL REVIEW LETTERS

Effects of cutoff functions of Tersoff potentials on molecular dynamics simulations of thermal transport

(2011) X W Zhou et al.   MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING

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

(2011) Jörg Behler   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

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

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

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

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