Quantum chemical accuracy from density functional approximations via machine learning

Machine Learning for Molecular Simulation

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

Completing density functional theory by machine learning hidden messages from molecules

(2020) Ryo Nagai et al.   npj Computational Materials

Exploring chemical compound space with quantum-based machine learning

(2020) O. Anatole von Lilienfeld et al.   Nature Reviews Chemistry

Solving the electronic structure problem with machine learning

(2019) Anand Chandrasekaran et al.   npj Computational Materials

Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces

(2019) Huziel E. Sauceda et al.   JOURNAL OF CHEMICAL PHYSICS

Operators in quantum machine learning: Response properties in chemical space

(2019) Anders S. Christensen et al.   JOURNAL OF CHEMICAL PHYSICS

sGDML: Constructing accurate and data efficient molecular force fields using machine learning

(2019) Stefan Chmiela et al.   COMPUTER PHYSICS COMMUNICATIONS

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

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

Passage through a sub-diffusing geometrical bottleneck

(2019) K. L. Sebastian   JOURNAL OF CHEMICAL PHYSICS

Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning

(2019) Frank Noé et al.   SCIENCE

Density Functional Analysis: The Theory of Density-Corrected DFT

(2019) Stefan Vuckovic et al.   Journal of Chemical Theory and Computation

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

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

Neural-Network-Based Path Collective Variables for Enhanced Sampling of Phase Transformations

(2019) Jutta Rogal et al.   PHYSICAL REVIEW LETTERS

Convolutional neural networks for atomistic systems

(2018) Kevin Ryczko et al.   COMPUTATIONAL MATERIALS SCIENCE

Reinforced Adversarial Neural Computer for de Novo Molecular Design

(2018) Evgeny Putin et al.   Journal of Chemical Information and Modeling

SchNet – A deep learning architecture for molecules and materials

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

Alchemical and structural distribution based representation for universal quantum machine learning

(2018) Felix A. Faber et al.   JOURNAL OF CHEMICAL PHYSICS

Semi-local machine-learned kinetic energy density functional with third-order gradients of electron density

(2018) Junji Seino et al.   JOURNAL OF CHEMICAL PHYSICS

Predicting molecular properties with covariant compositional networks

(2018) Truong Son Hy et al.   JOURNAL OF CHEMICAL PHYSICS

Less is more: Sampling chemical space with active learning

(2018) Justin S. Smith et al.   JOURNAL OF CHEMICAL PHYSICS

Can exact conditions improve machine-learned density functionals?

(2018) Jacob Hollingsworth et al.   JOURNAL OF CHEMICAL PHYSICS

Solid harmonic wavelet scattering for predictions of molecule properties

(2018) Michael Eickenberg et al.   JOURNAL OF CHEMICAL PHYSICS

Many-Body Descriptors for Predicting Molecular Properties with Machine Learning: Analysis of Pairwise and Three-Body Interactions in Molecules

(2018) Wiktor Pronobis et al.   Journal of Chemical Theory and Computation

Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

(2018) Andrea Grisafi et al.   PHYSICAL REVIEW LETTERS

Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

(2018) Linfeng Zhang et al.   PHYSICAL REVIEW LETTERS

Ab initio molecular dynamics study of overtone excitations in formic acid and its water complex

(2018) Teemu Järvinen et al.   THEORETICAL CHEMISTRY ACCOUNTS

VAMPnets for deep learning of molecular kinetics

(2018) Andreas Mardt et al.   Nature Communications

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

Deep reinforcement learning for de novo drug design

(2018) Mariya Popova et al.   Science Advances

Quantifying Density Errors in DFT

(2018) Eunji Sim et al.   Journal of Physical Chemistry Letters

Double-slit photoelectron interference in strong-field ionization of the neon dimer

(2018) Maksim Kunitski et al.   Nature Communications

Transferable Machine-Learning Model of the Electron Density

(2018) Andrea Grisafi et al.   ACS Central Science

The many-body expansion combined with neural networks

(2017) Kun Yao et al.   JOURNAL OF CHEMICAL PHYSICS

Cheap but accurate calculation of chemical reaction rate constants from ab initio data, via system-specific, black-box force fields

(2017) Julien Steffen et al.   JOURNAL OF CHEMICAL PHYSICS

Structure, spectroscopy, and dynamics of the phenol-(water)2 cluster at low and high temperatures

(2017) Nagaprasad Reddy Samala et al.   JOURNAL OF CHEMICAL PHYSICS

Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error

(2017) Felix A. Faber et al.   Journal of Chemical Theory and Computation

Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials

(2017) S. T. John et al.   JOURNAL OF PHYSICAL CHEMISTRY B

Stochastic Neural Network Approach for Learning High-Dimensional Free Energy Surfaces

(2017) Elia Schneider et al.   PHYSICAL REVIEW LETTERS

Machine learning molecular dynamics for the simulation of infrared spectra

(2017) Michael Gastegger 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

Molecular graph convolutions: moving beyond fingerprints

(2016) Steven Kearnes et al.   JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN

Complete Assignment of the Infrared Spectrum of the Gas-Phase Protonated Ammonia Dimer

(2016) Huan Wang et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Comparing molecules and solids across structural and alchemical space

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

DFT: A Theory Full of Holes?

(2015) Aurora Pribram-Jones et al.   Annual Review of Physical Chemistry

Nonlinear gradient denoising: Finding accurate extrema from inaccurate functional derivatives

(2015) John C. Snyder et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Understanding machine-learned density functionals

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

Gaussian approximation potentials: A brief tutorial introduction

(2015) Albert P. Bartók et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach

(2015) Raghunathan Ramakrishnan et al.   Journal of Chemical Theory and Computation

Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules

(2015) Tristan Bereau et al.   Journal of Chemical Theory and Computation

Machine Learning for Quantum Mechanical Properties of Atoms in Molecules

(2015) Matthias Rupp et al.   Journal of Physical Chemistry Letters

Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

(2015) Katja Hansen et al.   Journal of Physical Chemistry Letters

Learning scheme to predict atomic forces and accelerate materials simulations

(2015) V. Botu et al.   PHYSICAL REVIEW B

Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces

(2015) Zhenwei Li et al.   PHYSICAL REVIEW LETTERS

Orbital-free bond breaking via machine learning

(2013) John C. Snyder et al.   JOURNAL OF CHEMICAL PHYSICS

Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies

(2013) Katja Hansen et al.   Journal of Chemical Theory and Computation

Machine learning of molecular electronic properties in chemical compound space

(2013) Grégoire Montavon et al.   NEW JOURNAL OF PHYSICS

Understanding and Reducing Errors in Density Functional Calculations

(2013) Min-Cheol Kim et al.   PHYSICAL REVIEW LETTERS

Finding Density Functionals with Machine Learning

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

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Atom-centered symmetry functions for constructing high-dimensional neural network potentials

(2011) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Quantum nature of the hydrogen bond

(2011) X.-Z. Li et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

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

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

Quantum Effects on Vibrational and Electronic Spectra of Hydrazine Studied by “On-the-Fly” ab Initio Ring Polymer Molecular Dynamics†

(2009) Anna Kaczmarek et al.   JOURNAL OF PHYSICAL CHEMISTRY A