Related references
Note: Only part of the references are listed.Accurate molecular polarizabilities with coupled cluster theory and machine learning
David M. Wilkins et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2019)
Unmasking Clever Hans predictors and assessing what machines really learn
Sebastian Lapuschkin et al.
NATURE COMMUNICATIONS (2019)
A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules
Lixue Cheng et al.
JOURNAL OF CHEMICAL PHYSICS (2019)
Data-Driven Acceleration of the Coupled-Cluster Singles and Doubles Iterative Solver
Jacob Townsend et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2019)
SchNetPack: A Deep Learning Toolbox For Atomistic Systems
K. T. Schutt et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2019)
Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning
Evgeny Podryabinkin et al.
PHYSICAL REVIEW B (2019)
Deep learning and density-functional theory
Kevin Ryczko et al.
PHYSICAL REVIEW A (2019)
Machine Learning for Organic Synthesis: Are Robots Replacing Chemists?
Boris Maryasin et al.
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION (2018)
Quantum Machine Learning in Chemical Compound Space
O. Anatole von Lilienfeld
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION (2018)
Methods for interpreting and understanding deep neural networks
Gregoire Montavon et al.
DIGITAL SIGNAL PROCESSING (2018)
SchNet - A deep learning architecture for molecules and materials
K. T. Schuett et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems
Andrea Grisafi et al.
PHYSICAL REVIEW LETTERS (2018)
Transferability in Machine Learning for Electronic Structure via the Molecular Orbital Basis
Matthew Welborn et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2018)
Towards exact molecular dynamics simulations with machine-learned force fields
Stefan Chmiela et al.
NATURE COMMUNICATIONS (2018)
A Density Functional Tight Binding Layer for Deep Learning of Chemical Hamiltonians
Haichen Li et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2018)
ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition
Dipendra Jha et al.
SCIENTIFIC REPORTS (2018)
Solving the quantum many-body problem with artificial neural networks
Giuseppe Carleo et al.
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)
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)
Machine-learned approximations to Density Functional Theory Hamiltonians
Ganesh Hegde et al.
SCIENTIFIC REPORTS (2017)
Active learning of linearly parametrized interatomic potentials
Evgeny V. Podryabinkin et al.
COMPUTATIONAL MATERIALS SCIENCE (2017)
Critical analysis of fragment-orbital DFT schemes for the calculation of electronic coupling values
Christoph Schober et al.
JOURNAL OF CHEMICAL PHYSICS (2016)
Guiding ab initio calculations by alchemical derivatives
M. To Baben et al.
JOURNAL OF CHEMICAL PHYSICS (2016)
Comparing molecules and solids across structural and alchemical space
Sandip De et al.
PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2016)
Ab initio tensorial electronic friction for molecules on metal surfaces: Nonadiabatic vibrational relaxation
Reinhard J. Maurer et al.
PHYSICAL REVIEW B (2016)
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
Sebastian Bach et al.
PLOS ONE (2015)
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Matthias Rupp et al.
PHYSICAL REVIEW LETTERS (2012)
The ORCA program system
Frank Neese
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE (2012)
Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
Albert P. Bartok et al.
PHYSICAL REVIEW LETTERS (2010)
An improved neural network method for solving the Schrodinger equation1
Sergei Manzhos et al.
CANADIAN JOURNAL OF CHEMISTRY (2009)
Permutationally invariant potential energy surfaces in high dimensionality
Bastiaan J. Braams et al.
INTERNATIONAL REVIEWS IN PHYSICAL CHEMISTRY (2009)
Accurate sampling using Langevin dynamics
Giovanni Bussi et al.
PHYSICAL REVIEW E (2007)
Generalized neural-network representation of high-dimensional potential-energy surfaces
Joerg Behler et al.
PHYSICAL REVIEW LETTERS (2007)
Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for H to Rn: Design and assessment of accuracy
F Weigend et al.
PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2005)
Molecule intrinsic minimal basis sets.: I.: Exact resolution of ab initio optimized molecular orbitals in terms of deformed atomic minimal-basis orbitals
WC Lu et al.
JOURNAL OF CHEMICAL PHYSICS (2004)
Numerical solution of the Schrodinger equation by neural network and genetic algorithm
M Sugawara
COMPUTER PHYSICS COMMUNICATIONS (2001)
Share Paper
