Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

Unmasking Clever Hans predictors and assessing what machines really learn

(2019) Sebastian Lapuschkin et al.   Nature Communications

Accurate molecular polarizabilities with coupled cluster theory and machine learning

(2019) David M. Wilkins et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules

(2019) Lixue Cheng et al.   JOURNAL OF CHEMICAL PHYSICS

Data-Driven Acceleration of the Coupled-Cluster Singles and Doubles Iterative Solver

(2019) Jacob Townsend et al.   Journal of Physical Chemistry Letters

Machine Learning for Organic Synthesis: Are Robots Replacing Chemists?

(2018) Boris Maryasin et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Quantum Machine Learning in Chemical Compound Space

(2018) O. Anatole von Lilienfeld   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Methods for interpreting and understanding deep neural networks

(2018) Grégoire Montavon et al.   DIGITAL SIGNAL PROCESSING

SchNet – A deep learning architecture for molecules and materials

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

Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

(2018) Andrea Grisafi et al.   PHYSICAL REVIEW LETTERS

Transferability in Machine Learning for Electronic Structure via the Molecular Orbital Basis

(2018) Matthew Welborn et al.   Journal of Chemical Theory and Computation

Towards exact molecular dynamics simulations with machine-learned force fields

(2018) Stefan Chmiela et al.   Nature Communications

A Density Functional Tight Binding Layer for Deep Learning of Chemical Hamiltonians

(2018) Haichen Li et al.   Journal of Chemical Theory and Computation

SchNetPack: A Deep Learning Toolbox For Atomistic Systems

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

ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition

(2018) Dipendra Jha et al.   Scientific Reports

Solving the quantum many-body problem with artificial neural networks

(2017) Giuseppe Carleo et al.   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

Bypassing the Kohn-Sham equations with machine learning

(2017) Felix Brockherde et al.   Nature Communications

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

Machine-learned approximations to Density Functional Theory Hamiltonians

(2017) Ganesh Hegde et al.   Scientific Reports

Critical analysis of fragment-orbital DFT schemes for the calculation of electronic coupling values

(2016) Christoph Schober et al.   JOURNAL OF CHEMICAL PHYSICS

Guiding ab initio calculations by alchemical derivatives

(2016) M. to Baben et al.   JOURNAL OF CHEMICAL PHYSICS

Comparing molecules and solids across structural and alchemical space

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

On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation

(2015) Sebastian Bach et al.   PLoS One

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

The ORCA program system

(2011) Frank Neese   Wiley Interdisciplinary Reviews-Computational Molecular Science

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

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

An improved neural network method for solving the Schrödinger equation

(2009) Sergei Manzhos et al.   CANADIAN JOURNAL OF CHEMISTRY

Permutationally invariant potential energy surfaces in high dimensionality

(2009) Bastiaan J. Braams et al.   INTERNATIONAL REVIEWS IN PHYSICAL CHEMISTRY