Transferability of machine learning potentials: Protonated water neural network potential applied to the protonated water hexamer

PiNN: A Python Library for Building Atomic Neural Networks of Molecules and Materials

(2020) Yunqi Shao et al.   Journal of Chemical Information and Modeling

Machine learning for interatomic potential models

(2020) Tim Mueller et al.   JOURNAL OF CHEMICAL PHYSICS

An accurate and transferable machine learning potential for carbon

(2020) Patrick Rowe et al.   JOURNAL OF CHEMICAL PHYSICS

Neural Network Potential Energy Surfaces for Small Molecules and Reactions

(2020) Sergei Manzhos et al.   CHEMICAL REVIEWS

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

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

(2019) Volker L. Deringer et al.   ADVANCED MATERIALS

Automated Fitting of Neural Network Potentials at Coupled Cluster Accuracy: Protonated Water Clusters as Testing Ground

(2019) Christoph Schran et al.   Journal of Chemical Theory and Computation

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

(2018) Linfeng Zhang et al.   PHYSICAL REVIEW LETTERS

Benchmark Electronic Structure Calculations for H3O+(H2O)n, n = 0–5, Clusters and Tests of an Existing 1,2,3-Body Potential Energy Surface with a New 4-Body Correction

(2018) Joseph P. Heindel et al.   Journal of Chemical Theory and Computation

Converged Colored Noise Path Integral Molecular Dynamics Study of the Zundel Cation Down to Ultralow Temperatures at Coupled Cluster Accuracy

(2018) Christoph Schran et al.   Journal of Chemical Theory and Computation

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

Machine learning molecular dynamics for the simulation of infrared spectra

(2017) Michael Gastegger et al.   Chemical Science

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

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

Quantum-chemical insights from deep tensor neural networks

(2017) Kristof T. Schütt et al.   Nature Communications

Machine learning unifies the modeling of materials and molecules

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

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

Amp : A modular approach to machine learning in atomistic simulations

(2016) Alireza Khorshidi et al.   COMPUTER PHYSICS COMMUNICATIONS

Comparing the accuracy of high-dimensional neural network potentials and the systematic molecular fragmentation method: A benchmark study for all-trans alkanes

(2016) Michael Gastegger et al.   JOURNAL OF CHEMICAL PHYSICS

Perspective: Machine learning potentials for atomistic simulations

(2016) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Quantum Thermal Bath for Path Integral Molecular Dynamics Simulation

(2016) Fabien Brieuc et al.   Journal of Chemical Theory and Computation

Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials

(2016) Alexander V. Shapeev   MULTISCALE MODELING & SIMULATION

Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials

(2015) A.P. Thompson et al.   JOURNAL OF COMPUTATIONAL PHYSICS

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

Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces

(2015) Zhenwei Li et al.   PHYSICAL REVIEW LETTERS

Reactive Many-Body Expansion for a Protonated Water Cluster

(2013) Peter Pinski et al.   Journal of Chemical Theory and Computation

Isomer-Selective Detection of Hydrogen-Bond Vibrations in the Protonated Water Hexamer

(2013) Nadja Heine et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

cp2k: atomistic simulations of condensed matter systems

(2013) Jürg Hutter et al.   Wiley Interdisciplinary Reviews-Computational Molecular Science

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Atom-centered symmetry functions for constructing high-dimensional neural network potentials

(2011) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

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

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

Simplified CCSD(T)-F12 methods: Theory and benchmarks

(2009) Gerald Knizia et al.   JOURNAL OF CHEMICAL PHYSICS

A Hierarchical Approach to Study the Thermal Behavior of Protonated Water Clusters H+(H2O)n

(2009) Quoc Chinh Nguyen et al.   Journal of Chemical Theory and Computation