Gaussian approximation potentials: A brief tutorial introduction

Development of a “First-Principles” Water Potential with Flexible Monomers. III. Liquid Phase Properties

(2014) Gregory R. Medders et al.   Journal of Chemical Theory and Computation

Accuracy and transferability of Gaussian approximation potential models for tungsten

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

First-principles energetics of water clusters and ice: A many-body analysis

(2013) M. J. Gillan et al.   JOURNAL OF CHEMICAL PHYSICS

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

(2013) Katja Hansen et al.   Journal of Chemical Theory and Computation

On representing chemical environments

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

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

Potential Energy Surfaces Fitted by Artificial Neural Networks

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