Machine Learning of Analytical Electron Density in Large Molecules Through Message-Passing

Machine Learning for Molecular Simulation

(2020) Frank Noé et al.   Annual Review of Physical Chemistry

Quantum Chemistry in the Age of Machine Learning

(2020) Pavlo O. Dral   Journal of Physical Chemistry Letters

Building attention and edge message passing neural networks for bioactivity and physical–chemical property prediction

(2020) M. Withnall et al.   Journal of Cheminformatics

Non-Covalent Interactions Atlas Benchmark Data Sets: Hydrogen Bonding

(2020) Jan Řezáč   Journal of Chemical Theory and Computation

Fermionic neural-network states for ab-initio electronic structure

(2020) Kenny Choo et al.   Nature Communications

Completing density functional theory by machine learning hidden messages from molecules

(2020) Ryo Nagai et al.   npj Computational Materials

Analytical Model of Electron Density and Its Machine Learning Inference

(2020) Bruno Cuevas-Zuviría et al.   Journal of Chemical Information and Modeling

A deep neural network for molecular wave functions in quasi-atomic minimal basis representation

(2020) M. Gastegger et al.   JOURNAL OF CHEMICAL PHYSICS

The ORCA quantum chemistry program package

(2020) Frank Neese et al.   JOURNAL OF CHEMICAL PHYSICS

Machine Learning Approaches Toward Orbital-free Density Functional Theory: Simultaneous Training on the Kinetic Energy Density Functional and its Functional Derivative

(2020) Ralf Meyer et al.   Journal of Chemical Theory and Computation

Ground State Energy Functional with Hartree-Fock Efficiency and Chemical Accuracy

(2020) Yixiao Chen et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Machine learning accurate exchange and correlation functionals of the electronic density

(2020) Sebastian Dick et al.   Nature Communications

OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features

(2020) Zhuoran Qiao et al.   JOURNAL OF CHEMICAL PHYSICS

Quantum chemical accuracy from density functional approximations via machine learning

(2020) Mihail Bogojeski et al.   Nature Communications

Electron-Passing Neural Networks for Atomic Charge Prediction in Systems with Arbitrary Molecular Charge

(2020) Derek P. Metcalf et al.   Journal of Chemical Information and Modeling

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

(2019) K. T. Schütt et al.   Nature Communications

Learning models for electron densities with Bayesian regression

(2018) Eric Schmidt et al.   COMPUTATIONAL MATERIALS SCIENCE

Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning

(2018) Tristan Bereau et al.   JOURNAL OF CHEMICAL PHYSICS

Hierarchical modeling of molecular energies using a deep neural network

(2018) Nicholas Lubbers et al.   JOURNAL OF CHEMICAL PHYSICS

MoleculeNet: a benchmark for molecular machine learning

(2018) Zhenqin Wu et al.   Chemical Science

Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks

(2018) Benjamin Nebgen 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

Transferable Machine-Learning Model of the Electron Density

(2018) Andrea Grisafi et al.   ACS Central Science

Density functional theory is straying from the path toward the exact functional

(2017) Michael G. Medvedev et al.   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 unifies the modeling of materials and molecules

(2017) Albert P. Bartók et al.   Science Advances

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

Multipolar Electrostatic Energy Prediction for all 20 Natural Amino Acids Using Kriging Machine Learning

(2016) Timothy L. Fletcher et al.   Journal of Chemical Theory and Computation

The Monomer Electron Density Force Field (MEDFF): A Physically Inspired Model for Noncovalent Interactions

(2016) Steven Vandenbrande et al.   Journal of Chemical Theory and Computation

Understanding machine-learned density functionals

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

ZINC 15 – Ligand Discovery for Everyone

(2015) Teague Sterling et al.   Journal of Chemical Information and Modeling

FragBuilder: an efficient Python library to setup quantum chemistry calculations on peptides models

(2014) Anders S. Christensen et al.   PeerJ

Quantum chemistry structures and properties of 134 kilo molecules

(2014) Raghunathan Ramakrishnan et al.   Scientific Data

Subsystem density-functional theory

(2013) Christoph R. Jacob et al.   Wiley Interdisciplinary Reviews-Computational Molecular Science

Finding Density Functionals with Machine Learning

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

S66: A Well-balanced Database of Benchmark Interaction Energies Relevant to Biomolecular Structures

(2011) Jan Řezáč et al.   Journal of Chemical Theory and Computation