Deep Density: Circumventing the Kohn-Sham equations via symmetry preserving neural networks

Solving the electronic structure problem with machine learning

(2019) Anand Chandrasekaran et al.   npj Computational Materials

Numerical methods for Kohn–Sham density functional theory

(2019) Lin Lin et al.   ACTA NUMERICA

Machine learning electron density in sulfur crosslinked carbon nanotubes

(2018) John M. Alred et al.   COMPOSITES SCIENCE AND TECHNOLOGY

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

(2018) Han Wang et al.   COMPUTER PHYSICS COMMUNICATIONS

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

Transferable Machine-Learning Model of the Electron Density

(2018) Andrea Grisafi et al.   ACS Central Science

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

(2017) J. S. Smith et al.   Chemical Science

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

Optimization algorithm for the generation of ONCV pseudopotentials

(2015) Martin Schlipf et al.   COMPUTER PHYSICS COMMUNICATIONS

DGDFT: A massively parallel method for large scale density functional theory calculations

(2015) Wei Hu et al.   JOURNAL OF CHEMICAL PHYSICS

Machine learning of molecular electronic properties in chemical compound space

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

Optimized norm-conserving Vanderbilt pseudopotentials

(2013) D. R. Hamann   PHYSICAL REVIEW B

Elliptic Preconditioner for Accelerating the Self-Consistent Field Iteration in Kohn--Sham Density Functional Theory

(2013) Lin Lin et al.   SIAM JOURNAL ON SCIENTIFIC COMPUTING

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

\mathcal{O}(N) methods in electronic structure calculations

(2012) D R Bowler et al.   REPORTS ON PROGRESS IN PHYSICS

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

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