Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels

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

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

Quantum-chemical insights from deep tensor neural networks

(2017) Kristof T. Schütt et al.   Nature Communications

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

Machine learning for atomic forces in a crystalline solid: Transferability to various temperatures

(2016) Teppei Suzuki et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Communication: Fitting potential energy surfaces with fundamental invariant neural network

(2016) Kejie Shao 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

Perspective: Machine learning potentials for atomistic simulations

(2016) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Multiscale Quantum Mechanics/Molecular Mechanics Simulations with Neural Networks

(2016) Lin Shen et al.   Journal of Chemical Theory and Computation

Efficient non-parametric fitting of potential energy surfaces for polyatomic molecules with Gaussian processes

(2016) Jie Cui et al.   JOURNAL OF PHYSICS B-ATOMIC MOLECULAR AND OPTICAL PHYSICS

A global ab initio dipole moment surface for methyl chloride

(2016) Alec Owens et al.   JOURNAL OF QUANTITATIVE SPECTROSCOPY & RADIATIVE TRANSFER

Machine Learning Energies of 2 Million Elpasolite(ABC2D6)Crystals

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

Machine learning exciton dynamics

(2016) Florian Häse et al.   Chemical Science

Machine learning for quantum mechanics in a nutshell

(2015) Matthias Rupp   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Constructing high-dimensional neural network potentials: A tutorial review

(2015) Jörg Behler   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Gaussian approximation potentials: A brief tutorial introduction

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

Automatic differentiation method for numerical construction of the rotational-vibrational Hamiltonian as a power series in the curvilinear internal coordinates using the Eckart frame

(2015) Andrey Yachmenev et al.   JOURNAL OF CHEMICAL PHYSICS

Accurate ab initio vibrational energies of methyl chloride

(2015) Alec Owens et al.   JOURNAL OF CHEMICAL PHYSICS

Electronic spectra from TDDFT and machine learning in chemical space

(2015) Raghunathan Ramakrishnan et al.   JOURNAL OF CHEMICAL PHYSICS

Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations

(2015) Pavlo O. Dral et al.   Journal of Chemical Theory and Computation

High-Dimensional Neural Network Potentials for Organic Reactions and an Improved Training Algorithm

(2015) Michael Gastegger et al.   Journal of Chemical Theory and Computation

Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach

(2015) Raghunathan Ramakrishnan et al.   Journal of Chemical Theory and Computation

Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules

(2015) Tristan Bereau 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

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

(2015) Katja Hansen et al.   Journal of Physical Chemistry Letters

Explicitly correlated MRCI-F12 potential energy surfaces for methane fit with several permutation invariant schemes and full-dimensional vibrational calculations

(2015) Moumita Majumder et al.   MOLECULAR PHYSICS

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

(2015) Zhenwei Li et al.   PHYSICAL REVIEW LETTERS

Adaptive machine learning framework to accelerateab initiomolecular dynamics

(2014) Venkatesh Botu et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

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

Quantum chemistry structures and properties of 134 kilo molecules

(2014) Raghunathan Ramakrishnan et al.   Scientific Data

First principles view on chemical compound space: Gaining rigorous atomistic control of molecular properties

(2013) O. Anatole von Lilienfeld   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Neural networks for local structure detection in polymorphic systems

(2013) Philipp Geiger 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

Machine-learning approach for one- and two-body corrections to density functional theory: Applications to molecular and condensed water

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

On representing chemical environments

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

A neural network potential-energy surface for the water dimer based on environment-dependent atomic energies and charges

(2012) Tobias Morawietz et al.   JOURNAL OF CHEMICAL PHYSICS

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

(2012) Nongnuch Artrith et al.   PHYSICAL REVIEW B

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Nucleation mechanism for the direct graphite-to-diamond phase transition

(2011) Rustam Z. Khaliullin et al.   NATURE MATERIALS

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

(2011) Jörg Behler   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Ab initioquality neural-network potential for sodium

(2010) Hagai Eshet et al.   PHYSICAL REVIEW B

Graphite-diamond phase coexistence study employing a neural-network mapping of theab initiopotential energy surface

(2010) Rustam Z. Khaliullin 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

Development of generalized potential-energy surfaces using many-body expansions, neural networks, and moiety energy approximations

(2009) M. Malshe et al.   JOURNAL OF CHEMICAL PHYSICS

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

Metadynamics Simulations of the High-Pressure Phases of Silicon Employing a High-Dimensional Neural Network Potential

(2008) Jörg Behler et al.   PHYSICAL REVIEW LETTERS