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

Potential energy surfaces from high fidelity fitting ofab initiopoints: the permutation invariant polynomial - neural network approach

(2016) Bin Jiang et al.   INTERNATIONAL REVIEWS IN PHYSICAL CHEMISTRY

Comparing the accuracy of high-dimensional neural network potentials and the systematic molecular fragmentation method: A benchmark study for all-trans alkanes

(2016) Michael Gastegger et al.   JOURNAL OF CHEMICAL PHYSICS

Permutation invariant potential energy surfaces for polyatomic reactions using atomistic neural networks

(2016) Brian Kolb et al.   JOURNAL OF CHEMICAL PHYSICS

Ab Initio Investigation of O–H Dissociation from the Al–OH2 Complex Using Molecular Dynamics and Neural Network Fitting

(2016) Thi H. Ho et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Machine Learning Energies of 2 Million Elpasolite(ABC2D6)Crystals

(2016) Felix A. Faber et al.   PHYSICAL REVIEW LETTERS

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

Machine learning exciton dynamics

(2016) Florian Häse et al.   Chemical Science

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

Constructing high-dimensional neural network potentials: A tutorial review

(2015) Jörg Behler   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB

(2015) James A. Maier et al.   Journal of Chemical Theory and Computation

Machine Learning for Quantum Mechanical Properties of Atoms in Molecules

(2015) Matthias Rupp et al.   Journal of Physical Chemistry Letters

Deep learning

(2015) Yann LeCun et al.   NATURE

Deep learning in neural networks: An overview

(2015) Jürgen Schmidhuber   NEURAL NETWORKS

Neural network-based approaches for building high dimensional and quantum dynamics-friendly potential energy surfaces

(2014) Sergei Manzhos et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Accuracy and transferability of Gaussian approximation potential models for tungsten

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

Quantum chemistry structures and properties of 134 kilo molecules

(2014) Raghunathan Ramakrishnan et al.   Scientific Data

Amphetamine, past and present – a pharmacological and clinical perspective

(2013) David J Heal et al.   JOURNAL OF PSYCHOPHARMACOLOGY

On representing chemical environments

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

Exploring chemistry with the fragment molecular orbital method

(2012) Dmitri G. Fedorov et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

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

Systematic optimization of long-range corrected hybrid density functionals

(2008) Jeng-Da Chai et al.   JOURNAL OF CHEMICAL PHYSICS

Application of the PM6 method to modeling proteins

(2008) James J. P. Stewart   JOURNAL OF MOLECULAR MODELING