Can exact conditions improve machine-learned density functionals?

Semi-local machine-learned kinetic energy density functional with third-order gradients of electron density

(2018) Junji Seino et al.   JOURNAL OF CHEMICAL PHYSICS

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

(2018) Kun Yao et al.   Chemical Science

Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network

(2017) Kun Yao et al.   Journal of Physical Chemistry Letters

Sliced Basis Density Matrix Renormalization Group for Electronic Structure

(2017) E. Miles Stoudenmire et al.   PHYSICAL REVIEW 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

DFT: A Theory Full of Holes?

(2015) Aurora Pribram-Jones et al.   Annual Review of Physical Chemistry

Understanding machine-learned density functionals

(2015) Li Li et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Understanding kernel ridge regression: Common behaviors from simple functions to density functionals

(2015) Kevin Vu et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Machine Learning for Quantum Mechanical Properties of Atoms in Molecules

(2015) Matthias Rupp et al.   Journal of Physical Chemistry Letters

One-dimensional mimicking of electronic structure: The case for exponentials

(2015) Thomas E. Baker et al.   PHYSICAL REVIEW B

Density-Functional Theory for Strongly Correlated Bosonic and Fermionic Ultracold Dipolar and Ionic Gases

(2015) F. Malet et al.   PHYSICAL REVIEW LETTERS

Strongly Constrained and Appropriately Normed Semilocal Density Functional

(2015) Jianwei Sun et al.   PHYSICAL REVIEW LETTERS

Quest for a universal density functional: the accuracy of density functionals across a broad spectrum of databases in chemistry and physics

(2014) R. Peverati et al.   PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

Orbital-free bond breaking via machine learning

(2013) John C. Snyder et al.   JOURNAL OF CHEMICAL PHYSICS

DFT in a nutshell

(2012) Kieron Burke et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Finding Density Functionals with Machine Learning

(2012) John C. Snyder et al.   PHYSICAL REVIEW LETTERS

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

Entanglement and density-functional theory: Testing approximations on Hooke’s atom

(2008) J. P. Coe et al.   PHYSICAL REVIEW B