Discovering charge density functionals and structure-property relationships with PROPhet: A general framework for coupling machine learning and first-principles methods

Kinetic Energy of Hydrocarbons as a Function of Electron Density and Convolutional Neural Networks

(2016) Kun Yao et al.   Journal of Chemical Theory and Computation

Neural network molecular dynamics simulations of solid–liquid interfaces: water at low-index copper surfaces

(2016) Suresh Kondati Natarajan et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Grand canonical molecular dynamics simulations of Cu–Au nanoalloys in thermal equilibrium using reactive ANN potentials

(2015) Nongnuch Artrith et al.   COMPUTATIONAL MATERIALS SCIENCE

First principles phonon calculations in materials science

(2015) Atsushi Togo et al.   SCRIPTA MATERIALIA

Representing potential energy surfaces by high-dimensional neural network potentials

(2014) J Behler   JOURNAL OF PHYSICS-CONDENSED MATTER

Understanding the Composition and Activity of Electrocatalytic Nanoalloys in Aqueous Solvents: A Combination of DFT and Accurate Neural Network Potentials

(2014) Nongnuch Artrith et al.   NANO LETTERS

Thermal conductivity of group-IV semiconductors from a kinetic-collective model

(2014) C. de Tomas et al.   PROCEEDINGS 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

Permutation invariant polynomial neural network approach to fitting potential energy surfaces

(2013) Bin Jiang 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

Resolution-of-identity approach to Hartree–Fock, hybrid density functionals, RPA, MP2 andGWwith numeric atom-centered orbital basis functions

(2012) Xinguo Ren et al.   NEW JOURNAL OF PHYSICS

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

Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations

(2011) Jörg Behler   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

NWChem: A comprehensive and scalable open-source solution for large scale molecular simulations

(2010) M. Valiev et al.   COMPUTER PHYSICS COMMUNICATIONS

Ab initio calculations of optical absorption spectra: Solution of the Bethe–Salpeter equation within density matrix perturbation theory

(2010) Dario Rocca et al.   JOURNAL OF CHEMICAL PHYSICS

Potential Energy Surfaces Fitted by Artificial Neural Networks

(2010) Chris M. Handley et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Ab initio molecular simulations with numeric atom-centered orbitals

(2009) Volker Blum et al.   COMPUTER PHYSICS COMMUNICATIONS

Neural network approach to quantum-chemistry data: Accurate prediction of density functional theory energies

(2009) Roman M. Balabin et al.   JOURNAL OF CHEMICAL PHYSICS

QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials

(2009) Paolo Giannozzi et al.   JOURNAL OF PHYSICS-CONDENSED MATTER