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

Quantum Chemistry in the Age of Machine Learning

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

Systematic development of ab initio tight-binding models for hexagonal metals

(2020) J. Smutna et al.   Physical Review Materials

Operators in quantum machine learning: Response properties in chemical space

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

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

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

Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired Representation

(2019) Yaolong Zhang et al.   Journal of Physical Chemistry Letters

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

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

Machine learning and the physical sciences

(2019) Giuseppe Carleo et al.   REVIEWS OF MODERN PHYSICS

SchNet – A deep learning architecture for molecules and materials

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

Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

(2018) Andrea Grisafi et al.   PHYSICAL REVIEW LETTERS

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

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

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

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

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

SchNetPack: A Deep Learning Toolbox For Atomistic Systems

(2018) K. T. Schütt et al.   Journal of Chemical Theory and Computation

Deep learning for computational chemistry

(2017) Garrett B. Goh et al.   JOURNAL OF COMPUTATIONAL CHEMISTRY

High-Dimensional Atomistic Neural Network Potentials for Molecule–Surface Interactions: HCl Scattering from Au(111)

(2017) Brian Kolb et al.   Journal of Physical Chemistry Letters

Alchemical Predictions for Computational Catalysis: Potential and Limitations

(2017) Karthikeyan Saravanan et al.   Journal of Physical Chemistry Letters

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

Machine-learned approximations to Density Functional Theory Hamiltonians

(2017) Ganesh Hegde et al.   Scientific Reports

Acceleration of saddle-point searches with machine learning

(2016) Andrew A. Peterson   JOURNAL OF CHEMICAL PHYSICS

Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations

(2015) Pavlo O. Dral et al.   Journal of Chemical Theory and Computation

Maximally localized Wannier functions: Theory and applications

(2012) Nicola Marzari et al.   REVIEWS OF MODERN PHYSICS

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

(2011) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

The ORCA program system

(2011) Frank Neese   Wiley Interdisciplinary Reviews-Computational Molecular Science

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

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

An improved neural network method for solving the Schrödinger equation

(2009) Sergei Manzhos et al.   CANADIAN JOURNAL OF CHEMISTRY

Density-functional tight-binding for beginners

(2009) Pekka Koskinen et al.   COMPUTATIONAL MATERIALS SCIENCE