Artrith N - Search Results

1

Construction of high-dimensional neural network potentials using environment-dependent atom pairs.

Jose KV, Artrith N, Behler J. Jose KV, et al. Among authors: artrith n. J Chem Phys. 2012 May 21;136(19):194111. doi: 10.1063/1.4712397. J Chem Phys. 2012. PMID: 22612084

2

ænet-PyTorch: A GPU-supported implementation for machine learning atomic potentials training.

López-Zorrilla J, Aretxabaleta XM, Yeu IW, Etxebarria I, Manzano H, Artrith N. López-Zorrilla J, et al. Among authors: artrith n. J Chem Phys. 2023 Apr 28;158(16):164105. doi: 10.1063/5.0146803. J Chem Phys. 2023. PMID: 37096855

3

Understanding the composition and activity of electrocatalytic nanoalloys in aqueous solvents: a combination of DFT and accurate neural network potentials.

Artrith N, Kolpak AM. Artrith N, et al. Nano Lett. 2014 May 14;14(5):2670-6. doi: 10.1021/nl5005674. Epub 2014 May 1. Nano Lett. 2014. PMID: 24742028

4

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

Artrith N, Urban A, Ceder G. Artrith N, et al. J Chem Phys. 2018 Jun 28;148(24):241711. doi: 10.1063/1.5017661. J Chem Phys. 2018. PMID: 29960321 Free article.

5

AENET-LAMMPS and AENET-TINKER: Interfaces for accurate and efficient molecular dynamics simulations with machine learning potentials.

Chen MS, Morawietz T, Mori H, Markland TE, Artrith N. Chen MS, et al. Among authors: artrith n. J Chem Phys. 2021 Aug 21;155(7):074801. doi: 10.1063/5.0063880. J Chem Phys. 2021. PMID: 34418919

6

Engineering Transition-Metal-Coated Tungsten Carbides for Efficient and Selective Electrochemical Reduction of CO2 to Methane.

Wannakao S, Artrith N, Limtrakul J, Kolpak AM. Wannakao S, et al. Among authors: artrith n. ChemSusChem. 2015 Aug 24;8(16):2745-51. doi: 10.1002/cssc.201500245. Epub 2015 Jul 17. ChemSusChem. 2015. PMID: 26219085

7

Reduced overpotentials for electrocatalytic water splitting over Fe- and Ni-modified BaTiO₃.

Artrith N, Sailuam W, Limpijumnong S, Kolpak AM. Artrith N, et al. Phys Chem Chem Phys. 2016 Oct 26;18(42):29561-29570. doi: 10.1039/c6cp06031e. Phys Chem Chem Phys. 2016. PMID: 27748475

8

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

Urban A, Abdellahi A, Dacek S, Artrith N, Ceder G. Urban A, et al. Among authors: artrith n. Phys Rev Lett. 2017 Oct 27;119(17):176402. doi: 10.1103/PhysRevLett.119.176402. Epub 2017 Oct 25. Phys Rev Lett. 2017. PMID: 29219459 Free article.

9

Hidden structural and chemical order controls lithium transport in cation-disordered oxides for rechargeable batteries.

Ji H, Urban A, Kitchaev DA, Kwon DH, Artrith N, Ophus C, Huang W, Cai Z, Shi T, Kim JC, Kim H, Ceder G. Ji H, et al. Among authors: artrith n. Nat Commun. 2019 Feb 5;10(1):592. doi: 10.1038/s41467-019-08490-w. Nat Commun. 2019. PMID: 30723202 Free PMC article.

10

Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications.

Morawietz T, Artrith N. Morawietz T, et al. Among authors: artrith n. J Comput Aided Mol Des. 2021 Apr;35(4):557-586. doi: 10.1007/s10822-020-00346-6. Epub 2020 Oct 9. J Comput Aided Mol Des. 2021. PMID: 33034008 Free PMC article.

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