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

FCHL revisited: Faster and more accurate quantum machine learning

(2020) Anders S. Christensen et al.   JOURNAL OF CHEMICAL PHYSICS

Active learning of many-body configuration space: Application to the Cs+–water MB-nrg potential energy function as a case study

(2020) Yaoguang Zhai et al.   JOURNAL OF CHEMICAL PHYSICS

Psi4 1.4: Open-source software for high-throughput quantum chemistry

(2020) Daniel G. A. Smith et al.   JOURNAL OF CHEMICAL PHYSICS

Assessing conformer energies using electronic structure and machine learning methods

(2020) Dakota Folmsbee et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Graphics Processing Unit-Accelerated Semiempirical Born Oppenheimer Molecular Dynamics Using PyTorch

(2020) Guoqing Zhou et al.   Journal of Chemical Theory and Computation

Machine learning accurate exchange and correlation functionals of the electronic density

(2020) Sebastian Dick et al.   Nature Communications

GFN2-xTB—An Accurate and Broadly Parametrized Self-Consistent Tight-Binding Quantum Chemical Method with Multipole Electrostatics and Density-Dependent Dispersion Contributions

(2019) Christoph Bannwarth et al.   Journal of Chemical Theory and Computation

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

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

PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges

(2019) Oliver T. Unke et al.   Journal of Chemical Theory and Computation

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

(2019) Justin S. Smith et al.   Nature Communications

Regression Clustering for Improved Accuracy and Training Costs with Molecular-Orbital-Based Machine Learning

(2019) Lixue Cheng et al.   Journal of Chemical Theory and Computation

Operator Quantum Machine Learning: Navigating the Chemical Space of Response Properties

(2019) Anders S. Christensen et al.   CHIMIA

Correction to Analyzing Learned Molecular Representations for Property Prediction

(2019) Kevin Yang et al.   Journal of Chemical Information and Modeling

Hierarchical modeling of molecular energies using a deep neural network

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

Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions

(2018) Thuong T. Nguyen et al.   JOURNAL OF CHEMICAL PHYSICS

Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures

(2018) So Fujikake et al.   JOURNAL OF CHEMICAL PHYSICS

Making the Coupled Cluster Correlation Energy Machine-Learnable

(2018) Johannes T Margraf et al.   JOURNAL OF PHYSICAL CHEMISTRY A

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

(2018) Linfeng Zhang et al.   PHYSICAL REVIEW LETTERS

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

(2018) Kun Yao et al.   Chemical Science

MoleculeNet: a benchmark for molecular machine learning

(2018) Zhenqin Wu et al.   Chemical Science

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

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

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

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

Transferable Machine-Learning Model of the Electron Density

(2018) Andrea Grisafi et al.   ACS Central Science

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

(2017) Robert T. McGibbon et al.   JOURNAL OF CHEMICAL PHYSICS

A Robust and Accurate Tight-Binding Quantum Chemical Method for Structures, Vibrational Frequencies, and Noncovalent Interactions of Large Molecular Systems Parametrized for All spd-Block Elements (Z = 1–86)

(2017) Stefan Grimme et al.   Journal of Chemical Theory and Computation

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

Ultra-fast computation of electronic spectra for large systems by tight-binding based simplified Tamm-Dancoff approximation (sTDA-xTB)

(2016) Stefan Grimme et al.   JOURNAL OF CHEMICAL PHYSICS

Perspective: Machine learning potentials for atomistic simulations

(2016) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Molecular graph convolutions: moving beyond fingerprints

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

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

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

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

(2014) Piero Gasparotto et al.   JOURNAL OF CHEMICAL PHYSICS

Quantum chemistry structures and properties of 134 kilo molecules

(2014) Raghunathan Ramakrishnan et al.   Scientific Data

A simplified Tamm-Dancoff density functional approach for the electronic excitation spectra of very large molecules

(2013) Stefan Grimme   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

DrugBank 4.0: shedding new light on drug metabolism

(2013) Vivian Law et al.   NUCLEIC ACIDS RESEARCH

Excited states using the simplified Tamm–Dancoff-Approach for range-separated hybrid density functionals: development and application

(2013) Tobias Risthaus et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Long-Range Corrected Hybrid Density Functionals with Improved Dispersion Corrections

(2012) You-Sheng Lin et al.   Journal of Chemical Theory and Computation

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

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

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

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

(2009) Lorenz C. Blum et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY