Learning from the density to correct total energy and forces in first principle simulations

Solving the electronic structure problem with machine learning

(2019) Anand Chandrasekaran et al.   npj Computational Materials

Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces

(2019) Huziel E. Sauceda et al.   JOURNAL OF CHEMICAL PHYSICS

sGDML: Constructing accurate and data efficient molecular force fields using machine learning

(2019) Stefan Chmiela et al.   COMPUTER PHYSICS COMMUNICATIONS

Catalysis-Hub.org, an open electronic structure database for surface reactions

(2019) Kirsten T. Winther et al.   Scientific Data

SchNet – A deep learning architecture for molecules and materials

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

Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions

(2018) Thuong T. Nguyen et al.   JOURNAL OF CHEMICAL PHYSICS

Neural-network Kohn-Sham exchange-correlation potential and its out-of-training transferability

(2018) Ryo Nagai et al.   JOURNAL OF CHEMICAL PHYSICS

wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials

(2018) M. Gastegger et al.   JOURNAL OF CHEMICAL PHYSICS

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

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

Free Energy Profile of NaCl in Water: First-Principles Molecular Dynamics with SCAN and ωB97X-V Exchange–Correlation Functionals

(2018) Yi Yao et al.   Journal of Chemical Theory and Computation

Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics

(2018) Volker L. Deringer et al.   Journal of Physical Chemistry Letters

Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

(2018) Linfeng Zhang et al.   PHYSICAL REVIEW LETTERS

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

(2018) Rafael Gómez-Bombarelli et al.   ACS Central Science

The Alexandria library, a quantum-chemical database of molecular properties for force field development

(2018) Mohammad M. Ghahremanpour et al.   Scientific Data

Towards exact molecular dynamics simulations with machine-learned force fields

(2018) Stefan Chmiela et al.   Nature Communications

SchNetPack: A Deep Learning Toolbox For Atomistic Systems

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

Orientational Distribution of Free O-H Groups of Interfacial Water is Exponential

(2018) Shumei Sun et al.   PHYSICAL REVIEW LETTERS

Transferable Machine-Learning Model of the Electron Density

(2018) Andrea Grisafi et al.   ACS Central Science

Temperature-dependent vibrational spectra and structure of liquid water from classical and quantum simulations with the MB-pol potential energy function

(2017) Sandeep K. Reddy et al.   JOURNAL OF CHEMICAL PHYSICS

Deep learning for computational chemistry

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

Ab initio theory and modeling of water

(2017) Mohan Chen et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

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 of an exchange-correlation density functional for water

(2016) Michelle Fritz et al.   JOURNAL OF CHEMICAL PHYSICS

Perspective: How good is DFT for water?

(2016) Michael J. Gillan et al.   JOURNAL OF CHEMICAL PHYSICS

Perspective: Machine learning potentials for atomistic simulations

(2016) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

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

Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

(2015) Katja Hansen et al.   Journal of Physical Chemistry Letters

The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies

(2015) Scott Kirklin et al.   npj Computational Materials

The individual and collective effects of exact exchange and dispersion interactions on the ab initio structure of liquid water

(2014) Robert A. DiStasio et al.   JOURNAL OF CHEMICAL PHYSICS

Development of a “First Principles” Water Potential with Flexible Monomers. II: Trimer Potential Energy Surface, Third Virial Coefficient, and Small Clusters

(2014) Volodymyr Babin et al.   Journal of Chemical Theory and Computation

Exchange functional that tests the robustness of the plasmon description of the van der Waals density functional

(2014) Kristian Berland et al.   PHYSICAL REVIEW B

Quantum chemistry structures and properties of 134 kilo molecules

(2014) Raghunathan Ramakrishnan et al.   Scientific Data

A Critical Assessment of Two-Body and Three-Body Interactions in Water

(2013) Gregory R. Medders et al.   Journal of Chemical Theory and Computation

Development of a “First Principles” Water Potential with Flexible Monomers: Dimer Potential Energy Surface, VRT Spectrum, and Second Virial Coefficient

(2013) Volodymyr Babin et al.   Journal of Chemical Theory and Computation

Machine-learning approach for one- and two-body corrections to density functional theory: Applications to molecular and condensed water

(2013) Albert P. Bartók et al.   PHYSICAL REVIEW B

On representing chemical environments

(2013) Albert P. Bartók et al.   PHYSICAL REVIEW B

Understanding and Reducing Errors in Density Functional Calculations

(2013) Min-Cheol Kim et al.   PHYSICAL REVIEW LETTERS

Finding Density Functionals with Machine Learning

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

S66: A Well-balanced Database of Benchmark Interaction Energies Relevant to Biomolecular Structures

(2011) Jan Řezáč et al.   Journal of Chemical Theory and Computation