4.7 Article

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

Related references

Note: Only part of the references are listed.
Article Multidisciplinary Sciences

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)

Article Chemistry, Multidisciplinary

Transferable Machine-Learning Model of the Electron Density

Andrea Grisafi et al.

ACS CENTRAL SCIENCE (2019)

Article Chemistry, Physical

Hierarchical modeling of molecular energies using a deep neural network

Nicholas Lubbers et al.

JOURNAL OF CHEMICAL PHYSICS (2018)

Article Chemistry, Physical

Alchemical and structural distribution based representation for universal quantum machine learning

Felix A. Faber et al.

JOURNAL OF CHEMICAL PHYSICS (2018)

Article Chemistry, Physical

Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures

So Fujikake et al.

JOURNAL OF CHEMICAL PHYSICS (2018)

Article Physics, Multidisciplinary

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

Linfeng Zhang et al.

PHYSICAL REVIEW LETTERS (2018)

Article Chemistry, Multidisciplinary

The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics

Kun Yao et al.

CHEMICAL SCIENCE (2018)

Article Chemistry, Multidisciplinary

MoleculeNet: a benchmark for molecular machine learning

Zhenqin Wu et al.

CHEMICAL SCIENCE (2018)

Article Chemistry, Physical

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

Matthew Welborn et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2018)

Review Multidisciplinary Sciences

Machine learning for molecular and materials science

Keith T. Butler et al.

NATURE (2018)

Article Multidisciplinary Sciences

Planning chemical syntheses with deep neural networks and symbolic AI

Marwin H. S. Segler et al.

NATURE (2018)

Review Multidisciplinary Sciences

Inverse molecular design using machine learning: Generative models for matter engineering

Benjamin Sanchez-Lengeling et al.

SCIENCE (2018)

Article Multidisciplinary Sciences

Deep reinforcement learning for de novo drug design

Mariya Popova et al.

SCIENCE ADVANCES (2018)

Article Chemistry, Physical

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

Haichen Li et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2018)

Article Chemistry, Physical

Improving the accuracy of Moller-Plesset perturbation theory with neural networks

Robert T. McGibbon et al.

JOURNAL OF CHEMICAL PHYSICS (2017)

Article Chemistry, Multidisciplinary

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

J. S. Smith et al.

CHEMICAL SCIENCE (2017)

Article Multidisciplinary Sciences

To address surface reaction network complexity using scaling relations machine learning and DFT calculations

Zachary W. Ulissi et al.

NATURE COMMUNICATIONS (2017)

Article Multidisciplinary Sciences

Quantum-chemical insights from deep tensor neural networks

Kristof T. Schuett et al.

NATURE COMMUNICATIONS (2017)

Article Multidisciplinary Sciences

Bypassing the Kohn-Sham equations with machine learning

Felix Brockherde et al.

NATURE COMMUNICATIONS (2017)

Article Chemistry, Physical

Virtual screening of inorganic materials synthesis parameters with deep learning

Edward Kim et al.

NPJ COMPUTATIONAL MATERIALS (2017)

Article Chemistry, Multidisciplinary

Neural-Symbolic Machine Learning for Retrosynthesis and Reaction Prediction

Marwin H. S. Segler et al.

CHEMISTRY-A EUROPEAN JOURNAL (2017)

Review Chemistry, Medicinal

6 Deep Learning in Drug Discovery

Erik Gawehn et al.

MOLECULAR INFORMATICS (2016)

Article Chemistry, Physical

Perspective: Machine learning potentials for atomistic simulations

Joerg Behler

JOURNAL OF CHEMICAL PHYSICS (2016)

Article Computer Science, Interdisciplinary Applications

Gaussian processes with built-in dimensionality reduction: Applications to high-dimensional uncertainty propagation

Rohit Tripathy et al.

JOURNAL OF COMPUTATIONAL PHYSICS (2016)

Article Biochemistry & Molecular Biology

Molecular graph convolutions: moving beyond fingerprints

Steven Kearnes et al.

JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN (2016)

Article Multidisciplinary Sciences

Machine-learning-assisted materials discovery using failed experiments

Paul Raccuglia et al.

NATURE (2016)

Article Chemistry, Multidisciplinary

Neural Networks for the Prediction of Organic Chemistry Reactions

Jennifer N. Wei et al.

ACS CENTRAL SCIENCE (2016)

Review Pharmacology & Pharmacy

Machine-learning approaches in drug discovery: methods and applications

Antonio Lavecchia

DRUG DISCOVERY TODAY (2015)

Article Chemistry, Physical

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

Raghunathan Ramakrishnan et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2015)

Article Chemistry, Physical

Advances in molecular quantum chemistry contained in the Q-Chem 4 program package

Yihan Shao et al.

MOLECULAR PHYSICS (2015)

Article Chemistry, Physical

Recognizing molecular patterns by machine learning: An agnostic structural definition of the hydrogen bond

Piero Gasparotto et al.

JOURNAL OF CHEMICAL PHYSICS (2014)

Article Chemistry, Physical

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

Katja Hansen et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2013)

Article Chemistry, Physical

Intrinsic Atomic Orbitals: An Unbiased Bridge between Quantum Theory and Chemical Concepts

Gerald Knizia

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2013)

Article Physics, Multidisciplinary

Machine learning of molecular electronic properties in chemical compound space

Gregoire Montavon et al.

NEW JOURNAL OF PHYSICS (2013)

Article Physics, Multidisciplinary

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

Matthias Rupp et al.

PHYSICAL REVIEW LETTERS (2012)

Article Physics, Multidisciplinary

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

Albert P. Bartok et al.

PHYSICAL REVIEW LETTERS (2010)

Article Chemistry, Multidisciplinary

970 Million Druglike Small Molecules for Virtual Screening in the Chemical Universe Database GDB-13

Lorenz C. Blum et al.

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY (2009)

Article Physics, Fluids & Plasmas

Accurate sampling using Langevin dynamics

Giovanni Bussi et al.

PHYSICAL REVIEW E (2007)

Article Physics, Multidisciplinary

Generalized neural-network representation of high-dimensional potential-energy surfaces

Joerg Behler et al.

PHYSICAL REVIEW LETTERS (2007)

Article Chemistry, Physical

Fast Hartree-Fock theory using local density fitting approximations

R Polly et al.

MOLECULAR PHYSICS (2004)

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)

Article Statistics & Probability

Statistical modeling: The two cultures

L Breiman

STATISTICAL SCIENCE (2001)