Related references
Note: Only part of the references are listed.sGDML: Constructing accurate and data efficient molecular force fields using machine learning
Stefan Chmiela et al.
COMPUTER PHYSICS COMMUNICATIONS (2019)
Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces
Huziel E. Sauceda et al.
JOURNAL OF CHEMICAL PHYSICS (2019)
Solving the electronic structure problem with machine learning
Anand Chandrasekaran et al.
NPJ COMPUTATIONAL MATERIALS (2019)
Catalysis-Hub.org an open electronic structure database for surface reactions
Kirsten T. Winther et al.
SCIENTIFIC DATA (2019)
SchNetPack: A Deep Learning Toolbox For Atomistic Systems
K. T. Schutt et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2019)
Transferable Machine-Learning Model of the Electron Density
Andrea Grisafi et al.
ACS CENTRAL SCIENCE (2019)
One-dimensional vs. two-dimensional proton transport processes at solid-liquid zinc-oxide-water interfaces
Matti Hellstroem et al.
CHEMICAL SCIENCE (2019)
SchNet - A deep learning architecture for molecules and materials
K. T. Schuett et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions
Thuong T. Nguyen et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Neural-network Kohn-Sham exchange-correlation potential and its out-of-training transferability
Ryo Nagai et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
wACSF-Weighted atom-centered symmetry functions as descriptors in machine learning potentials
M. Gastegger et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Semi-local machine-learned kinetic energy density functional with third-order gradients of electron density
Junji Seino et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Free Energy Profile of NaCl in Water: First-Principles Molecular Dynamics with SCAN and omega B97X-V Exchange-Correlation Functionals
Yi Yao et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2018)
Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics
Volker L. Deringer et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2018)
Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics
Linfeng Zhang et al.
PHYSICAL REVIEW LETTERS (2018)
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
Rafael Gomez-Bombarelli et al.
ACS CENTRAL SCIENCE (2018)
The Alexandria library, a quantum-chemical database of molecular properties for force field development
Mohammad M. Ghahremanpour et al.
SCIENTIFIC DATA (2018)
Towards exact molecular dynamics simulations with machine-learned force fields
Stefan Chmiela et al.
NATURE COMMUNICATIONS (2018)
Orientational Distribution of Free O-H Groups of Interfacial Water is Exponential
Shumei Sun et al.
PHYSICAL REVIEW LETTERS (2018)
Temperature-dependent vibrational spectra and structure of liquid water from classical and quantum simulations with the MB-pol potential energy function
Sandeep K. Reddy et al.
JOURNAL OF CHEMICAL PHYSICS (2017)
Deep learning for computational chemistry
Garrett B. Goh et al.
JOURNAL OF COMPUTATIONAL CHEMISTRY (2017)
Ab initio theory and modeling of water
Mohan Chen et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2017)
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
J. S. Smith et al.
CHEMICAL SCIENCE (2017)
Bypassing the Kohn-Sham equations with machine learning
Felix Brockherde et al.
NATURE COMMUNICATIONS (2017)
Machine learning of accurate energy-conserving molecular force fields
Stefan Chmiela et al.
SCIENCE ADVANCES (2017)
Optimization of an exchange-correlation density functional for water
Michelle Fritz et al.
JOURNAL OF CHEMICAL PHYSICS (2016)
Perspective: How good is DFT for water?
Michael J. Gillan et al.
JOURNAL OF CHEMICAL PHYSICS (2016)
Perspective: Machine learning potentials for atomistic simulations
Joerg Behler
JOURNAL OF CHEMICAL PHYSICS (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)
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)
The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies
Scott Kirklin et al.
npj Computational Materials (2015)
The individual and collective effects of exact exchange and dispersion interactions on the ab initio structure of liquid water
Robert A. DiStasio et al.
JOURNAL OF CHEMICAL PHYSICS (2014)
Development of a First Principles Water Potential with Flexible Monomers. II: Trimer Potential Energy Surface, Third Virial Coefficient, and Small Clusters
Volodymyr Babin et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2014)
Exchange functional that tests the robustness of the plasmon description of the van der Waals density functional
Kristian Berland et al.
PHYSICAL REVIEW B (2014)
Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan et al.
SCIENTIFIC DATA (2014)
A Critical Assessment of Two-Body and Three-Body Interactions in Water
Gregory R. Medders et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2013)
Development of a First Principles Water Potential with Flexible Monomers: Dimer Potential Energy Surface, VRT Spectrum, and Second Virial Coefficient
Volodymyr Babin et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2013)
Machine-learning approach for one- and two-body corrections to density functional theory: Applications to molecular and condensed water
Albert P. Bartok et al.
PHYSICAL REVIEW B (2013)
On representing chemical environments
Albert P. Bartok et al.
PHYSICAL REVIEW B (2013)
Understanding and Reducing Errors in Density Functional Calculations
Min-Cheol Kim et al.
PHYSICAL REVIEW LETTERS (2013)
Finding Density Functionals with Machine Learning
John C. Snyder et al.
PHYSICAL REVIEW LETTERS (2012)
S66: A Well-balanced Database of Benchmark Interaction Energies Relevant to Biomolecular Structures
Jan Rezac et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2011)
Generalized neural-network representation of high-dimensional potential-energy surfaces
Joerg Behler et al.
PHYSICAL REVIEW LETTERS (2007)
A general purpose model for the condensed phases of water: TIP4P/2005
JLF Abascal et al.
JOURNAL OF CHEMICAL PHYSICS (2005)
Van der Waals density functional for general geometries -: art. no. 246401
M Dion et al.
PHYSICAL REVIEW LETTERS (2004)
Efficient mixed-force first-principles molecular dynamics
E Anglada et al.
PHYSICAL REVIEW E (2003)
The SIESTA method for ab initio order-N materials simulation
JM Soler et al.
JOURNAL OF PHYSICS-CONDENSED MATTER (2002)
Share Paper
