Nongnuch Artrith, Ph.D.

2019- Chemical Engineering Columbia University New York City  
Density Functional Theory, Artificial Neural Networks, Machine Learning, Materials Atomistic Simulations, ElectroCatalysis
"Nongnuch Artrith"
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Jörg Behler grad student
Gerbrand Ceder post-doc
Alexie M. Kolpak post-doc
Jingguang G. Chen research scientist (E-Tree)
Mark S. Hybertsen research scientist
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Ji H, Urban A, Kitchaev DA, et al. (2019) Hidden structural and chemical order controls lithium transport in cation-disordered oxides for rechargeable batteries. Nature Communications. 10: 592
Artrith N, Urban A, Ceder G. (2018) Constructing first-principles phase diagrams of amorphous LiSi using machine-learning-assisted sampling with an evolutionary algorithm. The Journal of Chemical Physics. 148: 241711
Urban A, Abdellahi A, Dacek S, et al. (2017) Electronic-Structure Origin of Cation Disorder in Transition-Metal Oxides. Physical Review Letters. 119: 176402
Artrith N, Sailuam W, Limpijumnong S, et al. (2016) Reduced overpotentials for electrocatalytic water splitting over Fe- and Ni-modified BaTiO3. Physical Chemistry Chemical Physics : Pccp
Elias JS, Artrith N, Bugnet M, et al. (2016) Elucidating the Nature of the Active Phase in Copper/Ceria Catalysts for CO Oxidation Acs Catalysis. 6: 1675-1679
Artrith N, Urban A. (2016) An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO2 Computational Materials Science. 114: 135-150
Wannakao S, Artrith N, Limtrakul J, et al. (2015) Engineering Transition-Metal-Coated Tungsten Carbides for Efficient and Selective Electrochemical Reduction of CO2 to Methane. Chemsuschem. 8: 2745-51
Artrith N, Kolpak AM. (2015) Grand canonical molecular dynamics simulations of Cu-Au nanoalloys in thermal equilibrium using reactive ANN potentials Computational Materials Science. 110: 20-28
Artrith N, Kolpak AM. (2014) Understanding the composition and activity of electrocatalytic nanoalloys in aqueous solvents: a combination of DFT and accurate neural network potentials. Nano Letters. 14: 2670-6
Artrith N, Hiller B, Behler J. (2013) Neural network potentials for metals and oxides - First applications to copper clusters at zinc oxide Physica Status Solidi (B) Basic Research. 250: 1191-1203
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