[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 | 525 | 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 | 395 | 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 | 324 | 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 | 319 | 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 | 311 | 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 | 223 | 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 | 207 | 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 | 164* | 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 | 157 | 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 | 145 | 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 | 136 | 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 | 136 | 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 | 123 | 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 | 117 | 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 | 99 | 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 | 81 | 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. https://doi.org/10.1088/2632-2153/abfd96, 2021 | 74 | 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 | 63 | 2009 |
Machine Learning for the Modeling of Interfaces in Energy Storage and Conversion Materials N Artrith Journal of Physics: Energy 1 (3), 032002, 2019 | 59 | 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 | 55 | 2020 |