Orbital Mixer: Using Atomic Orbital Features for Basis-Dependent Prediction of Molecular Wavefunctions

Orbital Mixer: Using Atomic Orbital Features for Basis-Dependent Prediction of Molecular Wavefunctions

(2022) Kirill Shmilovich et al.   Journal of Chemical Theory and Computation

Transfer learning with graph neural networks for optoelectronic properties of conjugated oligomers

(2021) Chee-Kong Lee et al.   JOURNAL OF CHEMICAL PHYSICS

Kohn-Sham Equations as Regularizer: Building Prior Knowledge into Machine-Learned Physics

(2021) Li Li et al.   PHYSICAL REVIEW LETTERS

Artificial Neural Networks as Trial Wave Functions for Quantum Monte Carlo

(2021) Jan Kessler et al.   Advanced Theory and Simulations

Learning to Approximate Density Functionals

(2021) Bhupalee Kalita et al.   ACCOUNTS OF CHEMICAL RESEARCH

Machine Learning Force Fields

(2021) Oliver T. Unke et al.   CHEMICAL REVIEWS

Transferable Multilevel Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multitask Learning

(2021) Ziteng Liu et al.   Journal of Chemical Information and Modeling

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

(2021) John A. Keith et al.   CHEMICAL REVIEWS

Variational Monte Carlo Calculations of A≤4 Nuclei with an Artificial Neural-Network Correlator Ansatz

(2021) Corey Adams et al.   PHYSICAL REVIEW LETTERS

A community-powered search of machine learning strategy space to find NMR property prediction models

(2021) Lars A. Bratholm et al.   PLoS One

Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture

(2021) Cheol Woo Park et al.   npj Computational Materials

Equivariant representations for molecular Hamiltonians and N-center atomic-scale properties

(2021) Jigyasa Nigam et al.   JOURNAL OF CHEMICAL PHYSICS

SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects

(2021) Oliver T. Unke et al.   Nature Communications

Machine learning activation energies of chemical reactions

(2021) Toby Lewis‐Atwell et al.   Wiley Interdisciplinary Reviews-Computational Molecular Science

Deep Learning for Optoelectronic Properties of Organic Semiconductors

(2020) Chengqiang Lu et al.   Journal of Physical Chemistry C

Machine Learning for Predicting the Band Gaps of ABX3 Perovskites from Elemental Properties

(2020) Vladislav Gladkikh et al.   Journal of Physical Chemistry C

Deep Learning of Activation Energies

(2020) Colin A. Grambow et al.   Journal of Physical Chemistry Letters

Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics

(2020) Julia Westermayr et al.   Journal of Physical Chemistry Letters

An Overview of Self-Consistent Field Calculations Within Finite Basis Sets

(2020) Susi Lehtola et al.   MOLECULES

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

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

Exploring chemical compound space with quantum-based machine learning

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

Deep learning for UV absorption spectra with SchNarc: First steps toward transferability in chemical compound space

(2020) J. Westermayr et al.   JOURNAL OF CHEMICAL PHYSICS

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

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

Quantum–mechanical property prediction of solvated drug molecules: what have we learned from a decade of SAMPL blind prediction challenges?

(2020) Nicolas Tielker et al.   JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN

Deep-neural-network solution of the electronic Schrödinger equation

(2020) Jan Hermann et al.   Nature Chemistry

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

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

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

A data ecosystem to support machine learning in materials science

(2019) Ben Blaiszik et al.   MRS Communications

Solving many-electron Schrödinger equation using deep neural networks

(2019) Jiequn Han et al.   JOURNAL OF COMPUTATIONAL PHYSICS

SchNet – A deep learning architecture for molecules and materials

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

Towards exact molecular dynamics simulations with machine-learned force fields

(2018) Stefan Chmiela et al.   Nature Communications

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

Py SCF: the Python-based simulations of chemistry framework

(2017) Qiming Sun et al.   Wiley Interdisciplinary Reviews-Computational Molecular Science

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

The Materials Data Facility: Data Services to Advance Materials Science Research

(2016) B. Blaiszik et al.   JOM

Broadly Accessible Self-Consistent Field Theory for Block Polymer Materials Discovery

(2016) Akash Arora et al.   MACROMOLECULES

Comparing molecules and solids across structural and alchemical space

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

Machine learning of molecular electronic properties in chemical compound space

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

Finding Density Functionals with Machine Learning

(2012) John C. Snyder 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