Related references
Note: Only part of the references are listed.Force Field Parametrization of Metal Ions from Statistical Learning Techniques
Francesco Fracchia et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2018)
Empirical Classification of Trajectory Data: An Opportunity for the Use of Machine Learning in Molecular Dynamics
Barry K. Carpenter et al.
JOURNAL OF PHYSICAL CHEMISTRY B (2018)
Is Multitask Deep Learning Practical for Pharma?
Bharath Ramsundar et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2017)
The many-body expansion combined with neural networks
Kun Yao et al.
JOURNAL OF CHEMICAL PHYSICS (2017)
Internal force corrections with machine learning for quantum mechanics/molecular mechanics simulations
Jingheng Wu et al.
JOURNAL OF CHEMICAL PHYSICS (2017)
Preface: Special Topic: From Quantum Mechanics to Force Fields
Jean-Philip Piquemal et al.
JOURNAL OF CHEMICAL PHYSICS (2017)
Representations in neural network based empirical potentials
Ekin D. Cubuk et al.
JOURNAL OF CHEMICAL PHYSICS (2017)
Toward chemical accuracy in the description of ion-water interactions through many-body representations. Alkali-water dimer potential energy surfaces
Marc Riera et al.
JOURNAL OF CHEMICAL PHYSICS (2017)
Improving the accuracy of Moller-Plesset perturbation theory with neural networks
Robert T. McGibbon et al.
JOURNAL OF CHEMICAL PHYSICS (2017)
Machine Learning Force Field Parameters from Ab lnitio Data
Ying Li et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2017)
Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
Felix A. Faber et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2017)
Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure-Property Relationships
Jon Paul Janet et al.
JOURNAL OF PHYSICAL CHEMISTRY A (2017)
Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials
S. T. John et al.
JOURNAL OF PHYSICAL CHEMISTRY B (2017)
Predicting Catalytic Activity of Nanoparticles by a DFT-Aided Machine-Learning Algorithm
Ryosuke Jinnouchi et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2017)
Molecular Origin of the Vibrational Structure of Ice Ih
Daniel R. Moberg et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2017)
Supervised Machine-Learning-Based Determination of Three-Dimensional Structure of Metallic Nanoparticles
Janis Timoshenko et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2017)
Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network
Kun Yao et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2017)
Accurate Neural Network Description of Surface Phonons in Reactive Gas-Surface Dynamics: N2 + Ru(0001)
Khosrow Shakouri et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2017)
Machine Learning Approach for Prediction and Understanding of Glass-Forming Ability
Y. T. Sun et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2017)
Learning physical descriptors for materials science by compressed sensing
Luca M. Ghiringhelli et al.
NEW JOURNAL OF PHYSICS (2017)
Machine learning for quantum dynamics: deep learning of excitation energy transfer properties
Florian Hase et al.
CHEMICAL SCIENCE (2017)
Predicting electronic structure properties of transition metal complexes with neural networks
Jon Paul Janet et al.
CHEMICAL SCIENCE (2017)
Machine learning molecular dynamics for the simulation of infrared spectra
Michael Gastegger et al.
CHEMICAL SCIENCE (2017)
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
J. S. Smith et al.
CHEMICAL SCIENCE (2017)
Quantum-chemical insights from deep tensor neural networks
Kristof T. Schuett et al.
NATURE COMMUNICATIONS (2017)
Universal fragment descriptors for predicting properties of inorganic crystals
Olexandr Isayev et al.
NATURE COMMUNICATIONS (2017)
Bypassing the Kohn-Sham equations with machine learning
Felix Brockherde et al.
NATURE COMMUNICATIONS (2017)
Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO2 Reduction
Zachary W. Ulissi et al.
ACS CATALYSIS (2017)
OpenMM 7: Rapid development of high performance algorithms for molecular dynamics
Peter Eastman et al.
PLOS COMPUTATIONAL BIOLOGY (2017)
ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules
Justin S. Smith et al.
SCIENTIFIC DATA (2017)
Data Descriptor: Machine-learned and codified synthesis parameters of oxide materials
Edward Kim et al.
SCIENTIFIC DATA (2017)
Machine learning of accurate energy-conserving molecular force fields
Stefan Chmiela et al.
SCIENCE ADVANCES (2017)
Discovering charge density functionals and structure-property relationships with PROPhet: A general framework for coupling machine learning and first-principles methods
Brian Kolb et al.
SCIENTIFIC REPORTS (2017)
Energy-free machine learning force field for aluminum
Ivan Kruglov et al.
SCIENTIFIC REPORTS (2017)
pDeep: Predicting MS/MS Spectra of Peptides with Deep Learning
Xie-Xuan Zhou et al.
ANALYTICAL CHEMISTRY (2017)
Deep learning and the Schrodinger equation
Kyle Mills et al.
PHYSICAL REVIEW A (2017)
First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems
Joerg Behler
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION (2017)
Machine learning based interatomic potential for amorphous carbon
Volker L. Deringer et al.
PHYSICAL REVIEW B (2017)
Understanding machine-learned density functionals
Li Li et al.
INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2016)
Amp: A modular approach to machine learning in atomistic simulations
Alireza Khorshidi et al.
COMPUTER PHYSICS COMMUNICATIONS (2016)
Acceleration of saddle-point searches with machine learning
Andrew A. Peterson
JOURNAL OF CHEMICAL PHYSICS (2016)
Communication: Fitting potential energy surfaces with fundamental invariant neural network
Kejie Shao et al.
JOURNAL OF CHEMICAL PHYSICS (2016)
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
Sandeep K. Reddy et al.
JOURNAL OF CHEMICAL PHYSICS (2016)
Exploration, Sampling, And Reconstruction of Free Energy Surfaces with Gaussian Process Regression
Letif Mones et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2016)
Kinetic Energy of Hydrocarbons as a Function of Electron Density and Convolutional Neural Networks
Kun Yao et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2016)
Neural Networks for the Prediction of Organic Chemistry Reactions
Jennifer N. Wei et al.
ACS CENTRAL SCIENCE (2016)
Pure density functional for strong correlation and the thermodynamic limit from machine learning
Li Li et al.
PHYSICAL REVIEW B (2016)
Materials Cartography: Representing and Mining Materials Space Using Structural and Electronic Fingerprints
Olexandr Isayev et al.
CHEMISTRY OF MATERIALS (2015)
Understanding kernel ridge regression: Common behaviors from simple functions to density functionals
Kevin Vu et al.
INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2015)
A permutationally invariant full-dimensional ab initio potential energy surface for the abstraction and exchange channels of the H + CH4 system
Jun Li et al.
JOURNAL OF CHEMICAL PHYSICS (2015)
On the representation of many-body interactions in water
Gregory R. Medders et al.
JOURNAL OF CHEMICAL PHYSICS (2015)
Permutationally Invariant Fitting of Many-Body, Non-covalent Interactions with Application to Three-Body Methane-Water-Water
Riccardo Conte et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2015)
Machine-Learning-Augmented Chemisorption Model for CO2 Electroreduction Catalyst Screening
Xianfeng Ma et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2015)
Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space
Katja Hansen et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2015)
Neural network-based approaches for building high dimensional and quantum dynamics-friendly potential energy surfaces
Sergei Manzhos et al.
INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2015)
Advances in molecular quantum chemistry contained in the Q-Chem 4 program package
Yihan Shao et al.
MOLECULAR PHYSICS (2015)
i-PI: A Python interface for ab initio path integral molecular dynamics simulations
Michele Ceriotti et al.
COMPUTER PHYSICS COMMUNICATIONS (2014)
Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry - the Harvard Clean Energy Project
Johannes Hachmann et al.
ENERGY & ENVIRONMENTAL SCIENCE (2014)
Effects of reagent rotational excitation on the H + CHD3 → H2 + CD3 reaction: A seven dimensional time-dependent wave packet study
Zhaojun Zhang et al.
JOURNAL OF CHEMICAL PHYSICS (2014)
Modeling electronic quantum transport with machine learning
Alejandro Lopez-Bezanilla et al.
PHYSICAL REVIEW B (2014)
How to represent crystal structures for machine learning: Towards fast prediction of electronic properties
K. T. Schuett et al.
PHYSICAL REVIEW B (2014)
Orbital-free bond breaking via machine learning
John C. Snyder et al.
JOURNAL OF CHEMICAL PHYSICS (2013)
A Critical Assessment of Two-Body and Three-Body Interactions in Water
Gregory R. Medders et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2013)
A Density-Functional Theory-Based Neural Network Potential for Water Clusters Including van der Waals Corrections
Tobias Morawietz et al.
JOURNAL OF PHYSICAL CHEMISTRY A (2013)
Accelerating materials property predictions using machine learning
Ghanshyam Pilania et al.
SCIENTIFIC REPORTS (2013)
A neural network potential-energy surface for the water dimer based on environment-dependent atomic energies and charges
Tobias Morawietz et al.
JOURNAL OF CHEMICAL PHYSICS (2012)
Finding Density Functionals with Machine Learning
John C. Snyder et al.
PHYSICAL REVIEW LETTERS (2012)
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Matthias Rupp et al.
PHYSICAL REVIEW LETTERS (2012)
Accurate and Efficient Method for Many-Body van der Waals Interactions
Alexandre Tkatchenko et al.
PHYSICAL REVIEW LETTERS (2012)
Accelerated computational discovery of high-performance materials for organic photovoltaics by means of cheminformatics
Roberto Olivares-Amaya et al.
ENERGY & ENVIRONMENTAL SCIENCE (2011)
The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid
Johannes Hachmann et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2011)
Nucleation mechanism for the direct graphite-to-diamond phase transition
Rustam Z. Khaliullin et al.
NATURE MATERIALS (2011)
Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations
Joerg Behler
PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2011)
High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide
Nongnuch Artrith et al.
PHYSICAL REVIEW B (2011)
Input vector optimization of feed-forward neural networks for fitting ab initio potential-energy databases
M. Malshe et al.
JOURNAL OF CHEMICAL PHYSICS (2010)
Potential Energy Surfaces Fitted by Artificial Neural Networks
Chris M. Handley et al.
JOURNAL OF PHYSICAL CHEMISTRY A (2010)
Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
Albert P. Bartok et al.
PHYSICAL REVIEW LETTERS (2010)
Fitting sparse multidimensional data with low-dimensional terms
Sergei Manzhos et al.
COMPUTER PHYSICS COMMUNICATIONS (2009)
Long-range corrected hybrid density functionals with damped atom-atom dispersion corrections
Jeng-Da Chai et al.
PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2008)
Well-tempered metadynamics: A smoothly converging and tunable free-energy method
Alessandro Barducci et al.
PHYSICAL REVIEW LETTERS (2008)
Generalized neural-network representation of high-dimensional potential-energy surfaces
Joerg Behler et al.
PHYSICAL REVIEW LETTERS (2007)
Semiempirical GGA-type density functional constructed with a long-range dispersion correction
Stefan Grimme
JOURNAL OF COMPUTATIONAL CHEMISTRY (2006)
Is the Ewald summation still necessary? Pairwise alternatives to the accepted standard for long-range electrostatics
Christopher J. Fennell et al.
JOURNAL OF CHEMICAL PHYSICS (2006)
A climbing image nudged elastic band method for finding saddle points and minimum energy paths
G Henkelman et al.
JOURNAL OF CHEMICAL PHYSICS (2000)
Share Paper
