Nongnuch Artrith
Nongnuch Artrith
Assistant Professor, Debye Institute for Nanomaterials Science, Utrecht University
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[Editor’s Choice] An Implementation of Artificial Neural-Network Potentials for Atomistic Materials Simulations: Performance for TiO2
N Artrith, A Urban
Computational Materials Science 114, 135-150, 2016
High-Dimensional Neural-Network Potentials for Multicomponent Systems: Applications to Zinc Oxide
N Artrith, T Morawietz, J Behler
Physical Review B 83 (15), 153101, 2011
Efficient and Accurate Machine-Learning Interpolation of Atomic Energies in Compositions with Many Species
N Artrith, A Urban, G Ceder
Physical Review B 96 (1), 014112, 2017
High-Dimensional Neural Network Potentials for Metal Surfaces: A Prototype Study for Copper
N Artrith, J Behler
Physical Review B 85 (4), 045439, 2012
Best Practices in Machine Learning for Chemistry
N Artrith, KT Butler, FX Coudert, S Han, O Isayev, A Jain, A Walsh
Nature Chemistry 13 (6), 505-508, 2021
Understanding the Composition and Activity of Electrocatalytic Nanoalloys in Aqueous Solvents: A Combination of DFT and Accurate Neural Network Potentials
N Artrith, AM Kolpak
Nano letters 14 (5), 2670-2676, 2014
Hidden Structural and Chemical Order Controls Lithium Transport in Cation-Disordered Oxides for Rechargeable Batteries
H Ji, A Urban, DA Kitchaev, DH Kwon, N Artrith, C Ophus, W Huang, Z Cai, ...
Nature communications 10 (1), 592, 2019
[Editor’s Pick] Constructing First-Principles Phase Diagrams of Amorphous LixSi Using Machine-Learning-Assisted Sampling with an Evolutionary Algorithm
N Artrith, A Urban, G Ceder
The Journal of Chemical Physics 148 (24), 241711, 2018
Electronic-Structure Origin of Cation Disorder in Transition-Metal Oxides
A Urban, A Abdellahi, S Dacek, N Artrith, G Ceder
Physical Review Letters 119 (17), 176402, 2017
Construction of High-Dimensional Neural Network Potentials Using Environment-Dependent Atom Pairs
KVJ Jose, N Artrith, J Behler
Journal of Chemical Physics 136 (19), 194111, 2012
Elucidating the Nature of the Active Phase in Copper/Ceria Catalysts for CO Oxidation
JS Elias, N Artrith, M Bugnet, L Giordano, GA Botton, AM Kolpak, ...
ACS Catalysis 6, 1675-1679, 2016
Neural Network Potentials for Metals and Oxides–First Applications to Copper Clusters at Zinc Oxide
N Artrith, B Hiller, J Behler
physica status solidi (b) 250 (6), 1191–1203, 2013
The Structural and Compositional Factors that Control the Li-ion Conductivity in LiPON Electrolytes
V Lacivita, N Artrith, G Ceder
Chemistry of Materials 30 (20), 7077-7090, 2018
Grand Canonical Molecular Dynamics Simulations of Cu–Au Nanoalloys in Thermal Equilibrium Using Reactive ANN Potentials
N Artrith, A Kolpak
Computational Materials Science 110, 20-28, 2015
Effect of Fluorination on Lithium Transport and Short‐Range Order in Disordered‐Rocksalt‐Type Lithium‐Ion Battery Cathodes
B Ouyang†, N Artrith†, Z Lun†, Z Jadidi, DA Kitchaev, H Ji, A Urban, ...
Advanced Energy Materials 10 (10), 1903240, 2020
Predicting the Activity and Selectivity of Bimetallic Metal Catalysts for Ethanol Reforming using Machine Learning
N Artrith, Z Lin, JG Chen
ACS Catalysis 10 (16), 9438−9444, 2020
Strategies for the Construction of Machine-Learning Potentials for Accurate and Efficient Atomic-Scale Simulations
AM Miksch, T Morawietz, J Kästner, A Urban, N Artrith
Mach. Learn.: Sci. Technol., 2021
Structure and Dynamics of Water Confined in Single-Wall Nanotubes
T Nanok, N Artrith, P Pantu, PA Bopp, J Limtrakul
The Journal of Physical Chemistry A 113 (10), 2103-2108, 2009
Machine Learning for the Modeling of Interfaces in Energy Storage and Conversion Materials
N Artrith
Journal of Physics: Energy 1 (3), 032002, 2019
Efficient Training of ANN Potentials by Including Atomic Forces via Taylor Expansion and Application to Water and a Transition-metal Oxide
AM Cooper, J Kästner, A Urban, N Artrith
npj Computational Materials 6, 54, 2020
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