Challenges for machine learning force fields in reproducing potential energy surfaces of flexible molecules

Dynamical strengthening of covalent and non-covalent molecular interactions by nuclear quantum effects at finite temperature

(2021) Huziel E. Sauceda et al.   Nature Communications

FCHL revisited: Faster and more accurate quantum machine learning

(2020) Anders S. Christensen et al.   JOURNAL OF CHEMICAL PHYSICS

Suppression of Electrochemically Driven Phase Transitions in Nanostructured MoS2 Pseudocapacitors Probed Using Operando X-ray Diffraction

(2019) John B. Cook et al.   ACS Nano

Dynamics of an adenine-adenine RNA conformational switch from discrete path sampling

(2019) Debayan Chakraborty et al.   JOURNAL OF CHEMICAL PHYSICS

Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces

(2019) Huziel E. Sauceda et al.   JOURNAL OF CHEMICAL PHYSICS

Operators in quantum machine learning: Response properties in chemical space

(2019) Anders S. Christensen et al.   JOURNAL OF CHEMICAL PHYSICS

sGDML: Constructing accurate and data efficient molecular force fields using machine learning

(2019) Stefan Chmiela et al.   COMPUTER PHYSICS COMMUNICATIONS

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

(2019) Justin S. Smith et al.   Nature Communications

Probing the transition state in enzyme catalysis by high-pressure NMR dynamics

(2019) John B. Stiller et al.   Nature Catalysis

Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning

(2019) Frank Noé et al.   SCIENCE

Incorporating long-range physics in atomic-scale machine learning

(2019) Andrea Grisafi et al.   JOURNAL OF CHEMICAL PHYSICS

Machine Learning in Computational Chemistry: An Evaluation of Method Performance for Nudged Elastic Band Calculations

(2019) Ralf Meyer et al.   Journal of Chemical Theory and Computation

Broadband transparent optical phase change materials for high-performance nonvolatile photonics

(2019) Yifei Zhang et al.   Nature Communications

Convolutional neural networks for atomistic systems

(2018) Kevin Ryczko et al.   COMPUTATIONAL MATERIALS SCIENCE

Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning

(2018) Tristan Bereau et al.   JOURNAL OF CHEMICAL PHYSICS

An atomistic fingerprint algorithm for learning ab initio molecular force fields

(2018) Yu-Hang Tang et al.   JOURNAL OF CHEMICAL PHYSICS

SchNet – A deep learning architecture for molecules and materials

(2018) K. T. Schütt et al.   JOURNAL OF CHEMICAL PHYSICS

Alchemical and structural distribution based representation for universal quantum machine learning

(2018) Felix A. Faber et al.   JOURNAL OF CHEMICAL PHYSICS

Solid harmonic wavelet scattering for predictions of molecule properties

(2018) Michael Eickenberg et al.   JOURNAL OF CHEMICAL PHYSICS

Many-Body Descriptors for Predicting Molecular Properties with Machine Learning: Analysis of Pairwise and Three-Body Interactions in Molecules

(2018) Wiktor Pronobis et al.   Journal of Chemical Theory and Computation

Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

(2018) Andrea Grisafi et al.   PHYSICAL REVIEW LETTERS

Nested Transition Path Sampling

(2018) Peter G. Bolhuis et al.   PHYSICAL REVIEW LETTERS

Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

(2018) Linfeng Zhang et al.   PHYSICAL REVIEW LETTERS

The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics

(2018) Kun Yao et al.   Chemical Science

VAMPnets for deep learning of molecular kinetics

(2018) Andreas Mardt et al.   Nature Communications

Towards exact molecular dynamics simulations with machine-learned force fields

(2018) Stefan Chmiela et al.   Nature Communications

i-PI 2.0: A universal force engine for advanced molecular simulations

(2018) Venkat Kapil et al.   COMPUTER PHYSICS COMMUNICATIONS

SchNetPack: A Deep Learning Toolbox For Atomistic Systems

(2018) K. T. Schütt et al.   Journal of Chemical Theory and Computation

The many-body expansion combined with neural networks

(2017) Kun Yao et al.   JOURNAL OF CHEMICAL PHYSICS

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

(2017) Pavlo O. Dral et al.   JOURNAL OF CHEMICAL PHYSICS

Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error

(2017) Felix A. Faber et al.   Journal of Chemical Theory and Computation

A Machine-Driven Hunt for Global Reaction Coordinates of Azobenzene Photoisomerization

(2017) Pedram Tavadze et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Machine learning molecular dynamics for the simulation of infrared spectra

(2017) Michael Gastegger et al.   Chemical Science

Bypassing the Kohn-Sham equations with machine learning

(2017) Felix Brockherde et al.   Nature Communications

Machine learning unifies the modeling of materials and molecules

(2017) Albert P. Bartók et al.   Science Advances

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

Communication: Fitting potential energy surfaces with fundamental invariant neural network

(2016) Kejie Shao et al.   JOURNAL OF CHEMICAL PHYSICS

Perspective: Machine learning potentials for atomistic simulations

(2016) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Comparing molecules and solids across structural and alchemical space

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

Gaussian approximation potentials: A brief tutorial introduction

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

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

Learning scheme to predict atomic forces and accelerate materials simulations

(2015) V. Botu et al.   PHYSICAL REVIEW B

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

(2015) Zhenwei Li et al.   PHYSICAL REVIEW LETTERS

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

Construction of high-dimensional neural network potentials using environment-dependent atom pairs

(2012) K. V. Jovan Jose et al.   JOURNAL OF CHEMICAL PHYSICS

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Protein folding kinetics and thermodynamics from atomistic simulation

(2012) S. Piana et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Photoisomerization in different classes of azobenzene

(2011) H. M. Dhammika Bandara et al.   CHEMICAL SOCIETY REVIEWS

Atom-centered symmetry functions for constructing high-dimensional neural network potentials

(2011) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

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

Ab initio molecular simulations with numeric atom-centered orbitals

(2009) Volker Blum et al.   COMPUTER PHYSICS COMMUNICATIONS

Quantum Chemical Investigation of Thermal Cis-to-Trans Isomerization of Azobenzene Derivatives: Substituent Effects, Solvent Effects, and Comparison to Experimental Data

(2009) Jadranka Dokić et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Accurate Molecular Van Der Waals Interactions from Ground-State Electron Density and Free-Atom Reference Data

(2009) Alexandre Tkatchenko et al.   PHYSICAL REVIEW LETTERS