Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles

Use of the complete basis set limit for computing highly accurate ab initio dipole moments

(2020) Eamon K. Conway et al.   JOURNAL OF CHEMICAL PHYSICS

Inexpensive modeling of quantum dynamics using path integral generalized Langevin equation thermostats

(2020) Venkat Kapil et al.   JOURNAL OF CHEMICAL PHYSICS

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning

(2019) Félix Musil et al.   Journal of Chemical Theory and Computation

Accurate molecular polarizabilities with coupled cluster theory and machine learning

(2019) David M. Wilkins et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Polarizable QM/MM Approach with Fluctuating Charges and Fluctuating Dipoles: The QM/FQFμ Model

(2019) Tommaso Giovannini et al.   Journal of Chemical Theory and Computation

Equation of State of Fluid Methane from First Principles with Machine Learning Potentials

(2019) Max Veit et al.   Journal of Chemical Theory and Computation

Operators in quantum machine learning: Response properties in chemical space

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

PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges

(2019) Oliver T. Unke et al.   Journal of Chemical Theory and Computation

Using Gaussian process regression to simulate the vibrational Raman spectra of molecular crystals

(2019) Nathaniel Raimbault et al.   NEW JOURNAL OF PHYSICS

Quantum mechanical static dipole polarizabilities in the QM7b and AlphaML showcase databases

(2019) Yang Yang et al.   Scientific Data

Incorporating long-range physics in atomic-scale machine learning

(2019) Andrea Grisafi et al.   JOURNAL OF CHEMICAL PHYSICS

Polarization in Kohn-Sham density-functional theory

(2018) Raffaele Resta   EUROPEAN PHYSICAL JOURNAL B

Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning

(2018) Tristan Bereau et al.   JOURNAL OF CHEMICAL PHYSICS

SchNet – A deep learning architecture for molecules and materials

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

Alchemical and structural distribution based representation for universal quantum machine learning

(2018) Felix A. Faber et al.   JOURNAL OF CHEMICAL PHYSICS

Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials

(2018) Giulio Imbalzano et al.   JOURNAL OF CHEMICAL PHYSICS

How Accurate Is Density Functional Theory at Predicting Dipole Moments? An Assessment Using a New Database of 200 Benchmark Values

(2018) Diptarka Hait et al.   Journal of Chemical Theory and Computation

Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

(2018) Andrea Grisafi et al.   PHYSICAL REVIEW LETTERS

Discovering a Transferable Charge Assignment Model Using Machine Learning

(2018) Andrew E. Sifain et al.   Journal of Physical Chemistry Letters

Chemical shifts in molecular solids by machine learning

(2018) Federico M. Paruzzo et al.   Nature Communications

Machine Learning a General-Purpose Interatomic Potential for Silicon

(2018) Albert P. Bartók et al.   Physical Review X

Machine learning molecular dynamics for the simulation of infrared spectra

(2017) Michael Gastegger et al.   Chemical Science

Machine learning unifies the modeling of materials and molecules

(2017) Albert P. Bartók et al.   Science Advances

Minimal Basis Iterative Stockholder: Atoms in Molecules for Force-Field Development

(2016) Toon Verstraelen et al.   Journal of Chemical Theory and Computation

How van der Waals interactions determine the unique properties of water

(2016) Tobias Morawietz et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Gaussian approximation potentials: A brief tutorial introduction

(2015) Albert P. Bartók et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Interatomic potentials for ionic systems with density functional accuracy based on charge densities obtained by a neural network

(2015) S. Alireza Ghasemi et al.   PHYSICAL REVIEW B

FOHI-D: An iterative Hirshfeld procedure including atomic dipoles

(2014) D. Geldof et al.   JOURNAL OF CHEMICAL PHYSICS

Benchmarking Quantum Chemical Methods for the Calculation of Molecular Dipole Moments and Polarizabilities

(2014) A. Leif Hickey et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Quantum chemistry structures and properties of 134 kilo molecules

(2014) Raghunathan Ramakrishnan et al.   Scientific Data

Hirshfeld-E Partitioning: AIM Charges with an Improved Trade-off between Robustness and Accurate Electrostatics

(2013) T. Verstraelen et al.   Journal of Chemical Theory and Computation

Machine learning of molecular electronic properties in chemical compound space

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

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

Nonlinear Light Scattering and Spectroscopy of Particles and Droplets in Liquids

(2012) Sylvie Roke et al.   Annual Review of Physical Chemistry

Improved Atoms-in-Molecule Charge Partitioning Functional for Simultaneously Reproducing the Electrostatic Potential and Chemical States in Periodic and Nonperiodic Materials

(2012) Thomas A. Manz et al.   Journal of Chemical Theory and Computation

A beginner's guide to the modern theory of polarization

(2012) Nicola A. Spaldin   JOURNAL OF SOLID STATE CHEMISTRY

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

Permutationally invariant potential energy surfaces in high dimensionality

(2009) Bastiaan J. Braams et al.   INTERNATIONAL REVIEWS IN PHYSICAL CHEMISTRY

Dynamically Polarizable Water Potential Based on Multipole Moments Trained by Machine Learning

(2009) Chris M. Handley et al.   Journal of Chemical Theory and Computation

Visualization and analysis of atomistic simulation data with OVITO–the Open Visualization Tool

(2009) Alexander Stukowski   MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING

Redefining the atom: atomic charge densities produced by an iterative stockholder approach

(2008) Timothy C. Lillestolen et al.   CHEMICAL COMMUNICATIONS

Beyond Point Charges: Dynamic Polarization from Neural Net Predicted Multipole Moments

(2008) Michael G. Darley et al.   Journal of Chemical Theory and Computation