Efficient training of ANN potentials by including atomic forces via Taylor expansion and application to water and a transition-metal oxide

GFN2-xTB—An Accurate and Broadly Parametrized Self-Consistent Tight-Binding Quantum Chemical Method with Multipole Electrostatics and Density-Dependent Dispersion Contributions

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

From Molecular Fragments to the Bulk: Development of a Neural Network Potential for MOF-5

(2019) Marco Eckhoff et al.   Journal of Chemical Theory and Computation

Parallel Multistream Training of High-Dimensional Neural Network Potentials

(2019) Andreas Singraber et al.   Journal of Chemical Theory and Computation

Machine-learned multi-system surrogate models for materials prediction

(2019) Chandramouli Nyshadham et al.   npj Computational Materials

Impact of lattice relaxations on phase transitions in a high-entropy alloy studied by machine-learning potentials

(2019) Tatiana Kostiuchenko et al.   npj Computational Materials

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

(2019) Justin S. Smith et al.   Nature Communications

An electrostatic spectral neighbor analysis potential for lithium nitride

(2019) Zhi Deng et al.   npj Computational Materials

Hiding in the Crowd: Spectral Signatures of Overcoordinated Hydrogen-Bond Environments

(2019) Tobias Morawietz et al.   Journal of Physical Chemistry Letters

Data‐Driven Materials Science: Status, Challenges, and Perspectives

(2019) Lauri Himanen et al.   Advanced Science

Recent advances and applications of machine learning in solid-state materials science

(2019) Jonathan Schmidt et al.   npj Computational 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

Constructing first-principles phase diagrams of amorphous LixSi using machine-learning-assisted sampling with an evolutionary algorithm

(2018) Nongnuch Artrith et al.   JOURNAL OF CHEMICAL PHYSICS

Potential energy surface interpolation with neural networks for instanton rate calculations

(2018) April M. Cooper et al.   JOURNAL OF CHEMICAL PHYSICS

Maximally resolved anharmonic OH vibrational spectrum of the water/ZnO(101¯0) interface from a high-dimensional neural network potential

(2018) Vanessa Quaranta et al.   JOURNAL OF CHEMICAL PHYSICS

The Interplay of Structure and Dynamics in the Raman Spectrum of Liquid Water over the Full Frequency and Temperature Range

(2018) Tobias Morawietz et al.   Journal of Physical Chemistry Letters

Metastable Structures in Cluster Catalysis from First-Principles: Structural Ensemble in Reaction Conditions and Metastability Triggered Reactivity

(2018) Geng Sun et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

The structural and compositional factors that control the Li-ion conductivity in LiPON electrolytes

(2018) Valentina Lacivita et al.   CHEMISTRY OF MATERIALS

Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning

(2018) Hongxiang Zong et al.   npj Computational Materials

Combining configurational energies and forces for molecular force field optimization

(2017) Lukas Vlcek et al.   JOURNAL OF CHEMICAL PHYSICS

Study of Li atom diffusion in amorphous Li3PO4 with neural network potential

(2017) Wenwen Li et al.   JOURNAL OF CHEMICAL PHYSICS

A Robust and Accurate Tight-Binding Quantum Chemical Method for Structures, Vibrational Frequencies, and Noncovalent Interactions of Large Molecular Systems Parametrized for All spd-Block Elements (Z = 1–86)

(2017) Stefan Grimme et al.   Journal of Chemical Theory and Computation

Quantum Dynamics and Spectroscopy of Ab Initio Liquid Water: The Interplay of Nuclear and Electronic Quantum Effects

(2017) Ondrej Marsalek et al.   Journal of Physical Chemistry Letters

Electronic-Structure Origin of Cation Disorder in Transition-Metal Oxides

(2017) Alexander Urban et al.   PHYSICAL REVIEW LETTERS

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

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

An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO 2

(2016) Nongnuch Artrith et al.   COMPUTATIONAL MATERIALS SCIENCE

Revised Damping Parameters for the D3 Dispersion Correction to Density Functional Theory

(2016) Daniel G. A. Smith et al.   Journal of Physical Chemistry Letters

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

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

Elucidating the Nature of the Active Phase in Copper/Ceria Catalysts for CO Oxidation

(2016) Joseph S. Elias et al.   ACS Catalysis

A new class of high capacity cation-disordered oxides for rechargeable lithium batteries: Li–Ni–Ti–Mo oxides

(2015) Jinhyuk Lee et al.   Energy & Environmental Science

Gaussian approximation potentials: A brief tutorial introduction

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

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

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

Strongly Constrained and Appropriately Normed Semilocal Density Functional

(2015) Jianwei Sun et al.   PHYSICAL REVIEW LETTERS

Density functional theory: Its origins, rise to prominence, and future

(2015) R. O. Jones   REVIEWS OF MODERN PHYSICS

First principles phonon calculations in materials science

(2015) Atsushi Togo et al.   SCRIPTA MATERIALIA

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

ChemShell-a modular software package for QM/MM simulations

(2013) Sebastian Metz et al.   Wiley Interdisciplinary Reviews-Computational Molecular Science

Turbomole

(2013) Filipp Furche et al.   Wiley Interdisciplinary Reviews-Computational Molecular Science

Perspective on density functional theory

(2012) Kieron Burke   JOURNAL OF CHEMICAL PHYSICS

Neural network potentials for metals and oxides - First applications to copper clusters at zinc oxide

(2012) Nongnuch Artrith et al.   PHYSICA STATUS SOLIDI B-BASIC SOLID STATE PHYSICS

High-dimensional neural network potentials for metal surfaces: A prototype study for copper

(2012) Nongnuch Artrith et al.   PHYSICAL REVIEW B

Neural network interatomic potential for the phase change material GeTe

(2012) Gabriele C. Sosso et al.   PHYSICAL REVIEW B

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

(2011) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide

(2011) Nongnuch Artrith et al.   PHYSICAL REVIEW B

A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu

(2010) Stefan Grimme et al.   JOURNAL OF CHEMICAL PHYSICS

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

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