Extending the accuracy of the SNAP interatomic potential form

An algorithm to use higher order invariants for modelling potential energy surface of nanoclusters

(2018) Shweta Jindal et al.   CHEMICAL PHYSICS LETTERS

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

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

Neural network potential for Al-Mg-Si alloys

(2017) Ryo Kobayashi et al.   PHYSICAL REVIEW MATERIALS

Accurate force field for molybdenum by machine learning large materials data

(2017) Chi Chen et al.   PHYSICAL REVIEW MATERIALS

Conceptual and practical bases for the high accuracy of machine learning interatomic potentials: Application to elemental titanium

(2017) Akira Takahashi et al.   PHYSICAL REVIEW MATERIALS

Perspective: Machine learning potentials for atomistic simulations

(2016) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Ab initiocalculations of grain boundaries in bcc metals

(2016) Daniel Scheiber et al.   MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING

Comparing molecules and solids across structural and alchemical space

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

The ReaxFF reactive force-field: development, applications and future directions

(2016) Thomas P Senftle et al.   npj Computational Materials

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

Gaussian approximation potentials: A brief tutorial introduction

(2015) Albert P. Bartók et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Permutation-invariant distance between atomic configurations

(2015) Grégoire Ferré et al.   JOURNAL OF CHEMICAL PHYSICS

Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials

(2015) A.P. Thompson et al.   JOURNAL OF COMPUTATIONAL PHYSICS

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

(2015) Zhenwei Li et al.   PHYSICAL REVIEW LETTERS

Representing potential energy surfaces by high-dimensional neural network potentials

(2014) J Behler   JOURNAL OF PHYSICS-CONDENSED MATTER

Accuracy and transferability of Gaussian approximation potential models for tungsten

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

Machine learning of molecular electronic properties in chemical compound space

(2013) Grégoire Montavon et al.   NEW JOURNAL OF PHYSICS

On representing chemical environments

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

Energy conserving, linear scaling Born-Oppenheimer molecular dynamics

(2012) M. J. Cawkwell et al.   JOURNAL OF CHEMICAL PHYSICS

Computational aspects of many-body potentials

(2012) Steven J. Plimpton et al.   MRS BULLETIN

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

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

(2011) Jörg Behler   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Charge-optimized many-body potential for the hafnium/hafnium oxide system

(2010) Tzu-Ray Shan 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