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

Is Multitask Deep Learning Practical for Pharma?

(2017) Bharath Ramsundar et al.   Journal of Chemical Information and Modeling

The many-body expansion combined with neural networks

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

Internal force corrections with machine learning for quantum mechanics/molecular mechanics simulations

(2017) Jingheng Wu et al.   JOURNAL OF CHEMICAL PHYSICS

Preface: Special Topic: From Quantum Mechanics to Force Fields

(2017) Jean-Philip Piquemal et al.   JOURNAL OF CHEMICAL PHYSICS

Representations in neural network based empirical potentials

(2017) Ekin D. Cubuk et al.   JOURNAL OF CHEMICAL PHYSICS

Toward chemical accuracy in the description of ion–water interactions through many-body representations. Alkali-water dimer potential energy surfaces

(2017) Marc Riera et al.   JOURNAL OF CHEMICAL PHYSICS

Improving the accuracy of Møller-Plesset perturbation theory with neural networks

(2017) Robert T. McGibbon et al.   JOURNAL OF CHEMICAL PHYSICS

Force Field Parametrization of Metal Ions from Statistical Learning Techniques

(2017) Francesco Fracchia et al.   Journal of Chemical Theory and Computation

Machine Learning Force Field Parameters from Ab Initio Data

(2017) Ying Li et al.   Journal of Chemical Theory and Computation

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

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

Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships

(2017) Jon Paul Janet et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Empirical Classification of Trajectory Data: An Opportunity for the Use of Machine Learning in Molecular Dynamics

(2017) Barry K. Carpenter et al.   JOURNAL OF PHYSICAL CHEMISTRY B

Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials

(2017) S. T. John et al.   JOURNAL OF PHYSICAL CHEMISTRY B

Predicting Catalytic Activity of Nanoparticles by a DFT-Aided Machine-Learning Algorithm

(2017) Ryosuke Jinnouchi et al.   Journal of Physical Chemistry Letters

Molecular Origin of the Vibrational Structure of Ice Ih

(2017) Daniel R. Moberg et al.   Journal of Physical Chemistry Letters

Supervised Machine-Learning-Based Determination of Three-Dimensional Structure of Metallic Nanoparticles

(2017) Janis Timoshenko et al.   Journal of Physical Chemistry Letters

Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network

(2017) Kun Yao et al.   Journal of Physical Chemistry Letters

Accurate Neural Network Description of Surface Phonons in Reactive Gas–Surface Dynamics: N2 + Ru(0001)

(2017) Khosrow Shakouri et al.   Journal of Physical Chemistry Letters

Machine Learning Approach for Prediction and Understanding of Glass-Forming Ability

(2017) Y. T. Sun et al.   Journal of Physical Chemistry Letters

Learning physical descriptors for materials science by compressed sensing

(2017) Luca M Ghiringhelli et al.   NEW JOURNAL OF PHYSICS

Machine learning for quantum dynamics: deep learning of excitation energy transfer properties

(2017) Florian Häse et al.   Chemical Science

Predicting electronic structure properties of transition metal complexes with neural networks

(2017) Jon Paul Janet et al.   Chemical Science

Machine learning molecular dynamics for the simulation of infrared spectra

(2017) Michael Gastegger et al.   Chemical Science

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

Universal fragment descriptors for predicting properties of inorganic crystals

(2017) Olexandr Isayev et al.   Nature Communications

Bypassing the Kohn-Sham equations with machine learning

(2017) Felix Brockherde et al.   Nature Communications

Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO2 Reduction

(2017) Zachary W. Ulissi et al.   ACS Catalysis

OpenMM 7: Rapid development of high performance algorithms for molecular dynamics

(2017) Peter Eastman et al.   PLoS Computational Biology

ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules

(2017) Justin S. Smith et al.   Scientific Data

Machine-learned and codified synthesis parameters of oxide materials

(2017) Edward Kim et al.   Scientific Data

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

Discovering charge density functionals and structure-property relationships with PROPhet: A general framework for coupling machine learning and first-principles methods

(2017) Brian Kolb et al.   Scientific Reports

Energy-free machine learning force field for aluminum

(2017) Ivan Kruglov et al.   Scientific Reports

Amp : A modular approach to machine learning in atomistic simulations

(2016) Alireza Khorshidi et al.   COMPUTER PHYSICS COMMUNICATIONS

Acceleration of saddle-point searches with machine learning

(2016) Andrew A. Peterson   JOURNAL OF CHEMICAL PHYSICS

Communication: Fitting potential energy surfaces with fundamental invariant neural network

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

On the accuracy of the MB-pol many-body potential for water: Interaction energies, vibrational frequencies, and classical thermodynamic and dynamical properties from clusters to liquid water and ice

(2016) Sandeep K. Reddy et al.   JOURNAL OF CHEMICAL PHYSICS

Exploration, Sampling, And Reconstruction of Free Energy Surfaces with Gaussian Process Regression

(2016) Letif Mones et al.   Journal of Chemical Theory and Computation

Kinetic Energy of Hydrocarbons as a Function of Electron Density and Convolutional Neural Networks

(2016) Kun Yao et al.   Journal of Chemical Theory and Computation

Neural Networks for the Prediction of Organic Chemistry Reactions

(2016) Jennifer N. Wei et al.   ACS Central Science

Materials Cartography: Representing and Mining Materials Space Using Structural and Electronic Fingerprints

(2015) Olexandr Isayev et al.   CHEMISTRY OF MATERIALS

Understanding machine-learned density functionals

(2015) Li Li et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Understanding kernel ridge regression: Common behaviors from simple functions to density functionals

(2015) Kevin Vu et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

A permutationally invariant full-dimensional ab initio potential energy surface for the abstraction and exchange channels of the H + CH4 system

(2015) Jun Li et al.   JOURNAL OF CHEMICAL PHYSICS

On the representation of many-body interactions in water

(2015) Gregory R. Medders et al.   JOURNAL OF CHEMICAL PHYSICS

Permutationally Invariant Fitting of Many-Body, Non-covalent Interactions with Application to Three-Body Methane–Water–Water

(2015) Riccardo Conte et al.   Journal of Chemical Theory and Computation

Machine-Learning-Augmented Chemisorption Model for CO2 Electroreduction Catalyst Screening

(2015) Xianfeng Ma 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

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

Effects of reagent rotational excitation on the H + CHD3 → H2 + CD3 reaction: A seven dimensional time-dependent wave packet study

(2014) Zhaojun Zhang et al.   JOURNAL OF CHEMICAL PHYSICS

Advances in molecular quantum chemistry contained in the Q-Chem 4 program package

(2014) Yihan Shao et al.   MOLECULAR PHYSICS

Modeling electronic quantum transport with machine learning

(2014) Alejandro Lopez-Bezanilla et al.   PHYSICAL REVIEW B

How to represent crystal structures for machine learning: Towards fast prediction of electronic properties

(2014) K. T. Schütt et al.   PHYSICAL REVIEW B

i-PI: A Python interface for ab initio path integral molecular dynamics simulations

(2013) Michele Ceriotti et al.   COMPUTER PHYSICS COMMUNICATIONS

Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry – the Harvard Clean Energy Project

(2013) Johannes Hachmann et al.   Energy & Environmental Science

Orbital-free bond breaking via machine learning

(2013) John C. Snyder et al.   JOURNAL OF CHEMICAL PHYSICS

A Critical Assessment of Two-Body and Three-Body Interactions in Water

(2013) Gregory R. Medders et al.   Journal of Chemical Theory and Computation

A Density-Functional Theory-Based Neural Network Potential for Water Clusters Including van der Waals Corrections

(2013) Tobias Morawietz et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Accelerating materials property predictions using machine learning

(2013) Ghanshyam Pilania et al.   Scientific Reports

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

Finding Density Functionals with Machine Learning

(2012) John C. Snyder et al.   PHYSICAL REVIEW LETTERS

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Accurate and Efficient Method for Many-Body van der Waals Interactions

(2012) Alexandre Tkatchenko et al.   PHYSICAL REVIEW LETTERS

Accelerated computational discovery of high-performance materials for organic photovoltaics by means of cheminformatics

(2011) Roberto Olivares-Amaya et al.   Energy & Environmental Science

The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid

(2011) Johannes Hachmann et al.   Journal of Physical Chemistry 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

High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide

(2011) Nongnuch Artrith et al.   PHYSICAL REVIEW B

Input vector optimization of feed-forward neural networks for fitting ab initio potential-energy databases

(2010) M. Malshe et al.   JOURNAL OF CHEMICAL PHYSICS

Potential Energy Surfaces Fitted by Artificial Neural Networks

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

Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

(2010) Albert P. Bartók et al.   PHYSICAL REVIEW LETTERS

Fitting sparse multidimensional data with low-dimensional terms

(2009) Sergei Manzhos et al.   COMPUTER PHYSICS COMMUNICATIONS

Long-range corrected hybrid density functionals with damped atom–atom dispersion corrections

(2008) Jeng-Da Chai et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Well-Tempered Metadynamics: A Smoothly Converging and Tunable Free-Energy Method

(2008) Alessandro Barducci et al.   PHYSICAL REVIEW LETTERS