Publications

(* = corresponding author, † = equal contributions)

2021

  1. N. Artrith*, K.T. Butler*, F.-X. Coudert*, S. Han*, O. Isayev*, A. Jain*, A. Walsh*,
    “Best Practices in Machine Learning for Chemistry”, Nat. Chem. 13 (2021) 505-508. Open Access .

  2. A.M. Miksch*, T. Morawietz, J. Kästner, A. Urban, N. Artrith*,
    “Strategies for the Construction of Machine-Learning Potentials for Accurate and Efficient Atomic-Scale Simulations”, Mach. Learn.: Sci. Technol. 2 (2021) 031001. Open Access .

  3. H. Guo*, Q. Wang, A. Stuke, A. Urban, N. Artrith*,
    “Accelerated Atomistic Modeling of Solid-State Battery Materials With Machine Learning”, Front. Energy Res. 9 (2021) 695902. Open Access .

  4. T. Morawietz* and N. Artrith*,
    “Machine Learning-Accelerated Quantum Mechanics-Based Atomistic Simulations for Industrial Applications”, J. Comput. Aided Mol. Des. 2 (2021) 031001 Open Access .

  5. N. Artrith*, J.A.G. Torres, A. Urban, M.S. Hybertsen*,
    “Data-driven Approach to Parameterize SCAN+U for an Accurate Description of 3d Transition Metal Oxide Thermochemistry”, submitted (2021) https://arxiv.org/abs/2102.01131. Preprint .

2020

  1. N. Artrith*,
    “Learning What Makes Catalysts Good”, Matter (Cell Press) 3 (2020) 985-986 .

  2. A.M. Cooper, J. Kästner, A. Urban, N. Artrith*,
    “Efficient training of ANN potentials by including atomic forces via Taylor expansion and application to water and a transition-metal oxide”, npj Comput Mater 6 (2020) 54. Open Access .
    The database can be obtained from the Materials Cloud repository .

  3. N. Artrith*, Zhexi Lin, Jingguang G. Chen,
    “Predicting the Activity and Selectivity of Bimetallic Metal Catalysts for Ethanol Reforming using Machine Learning”, ACS Catal. 10 (2020) 9438–9444 (Letter) . (preprint)

  4. D.H. Kwon*†, J. Lee†, N. Artrith, H. Kim, L. Wu, Z. Lun, Y. Tian, Y. Zhu, G. Ceder*,
    “The Impact of Surface Structure Transformations on the Performance of Li-Excess Cation-Disordered Rocksalt Cathodes”, Cell Reports Physical Science 1 (2020) 100187. Open Access .

  5. B. Ouyang†, N. Artrith†, Z. Lun†, Z. Jadidi, D. A. Kitchaev, H. Ji, A. Urban, G. Ceder*, (†=co-first authors)
    “Effect of Fluorination on Lithium Transport and Short‐Range Order in Disordered‐Rocksalt‐Type Lithium‐Ion Battery Cathodes”, Adv. Energy Mater. 10 (2020) 1903240 .

2019 and before

  1. N. Artrith*,
    “Machine Learning for the Modeling of Interfaces in Energy Storage and Conversion Materials”, J. Phys. Energy 1, (2019) 032002. Open Access .
  2. H. Ji, A. Urban, D.A. Kitchaev, D.H. Kwon, N. Artrith, C. Ophus, W. Huang, Z. Cai, T. Shi, J.C. Kim, G. Ceder*,
    “Hidden structural order controls Li-ion transport in cation-disordered oxides for rechargeable lithium batteries”, Nat. Commun. 10 (2019) 592 .
  3. N. Artrith*, A. Urban, Y. Wang, G. Ceder*,
    “Atomic-Scale Factors that Control the Rate Capability of Nanostructured Amorphous Si for High-Energy-Density Batteries”, https://arxiv.org/abs/1901.09272 (2019) .
  4. V. Lacivita*, N. Artrith, and G. Ceder*,
    “The Structural and Compositional Factors that Control the Li-Ion Conductivity in LiPON Electrolytes”, Chem. Mater. 30 (2018) 7077-7090. Open Access .
  5. N. Artrith*, A. Urban, and G. Ceder*,
    “Constructing First-Principles Phase Diagrams of Amorphous LixSi using Machine-Learning-Assisted Sampling with an Evolutionary Algorithm”, J. Chem. Phys. 148 (2018) 241711 . (Editor’s Pick) ( preprint )
  6. W. Huang, A. Urban, P. Xiao, Z. Rong, H. Das, T. Chen, N. Artrith, A. Toumar, G. Ceder*,
    “An L0L1-Norm Compressive Sensing Paradigm for the Construction of Sparse Predictive Lattice Models using Mixed Integer Quadratic Programming”, https://arxiv.org/abs/1807.10753 (2018).
  7. N. Artrith*, A. Urban, and G. Ceder*,
    “Efficient and Accurate Machine-Learning Interpolation of Atomic Energies in Compositions with Many Species”, Phys. Rev. B 96 (2017) 014112 . ( preprint )
  8. A. Urban*, A. Abdellahi†, S. Dacek†, N. Artrith†, and G. Ceder* (†=equal contributions),
    “The Electronic-Structure Origin of Cation Disorder in Transition-Metal Oxides”, Phys. Rev. Lett. 119 (2017) 176402 .
  9. S. Wannakao, N. Artrith, J. Limtrakul, A.M. Kolpak*,
    “Catalytic Activity and Product Selectivity Trends for Carbon Dioxide Electroreduction on Transition Metal- Coated Tungsten Carbides”, J. Phys. Chem. C 121 (2017) 20306 .
  10. J.S. Elias, N. Artrith, M. Bugnet, L. Giordano, G.A. Botton, A.M. Kolpak, and Y. Shao-Horn*,
    “Elucidating the Nature of the Active Phase in Copper/Ceria Catalysts for CO Oxidation”, ACS Catal. 6 (2016) 1675-1679 .
  11. N. Artrith* and A. Urban,
    “An Implementation of Artificial Neural-Network Potentials for Atomistic Materials Simulations: Performance for TiO2”, Comput. Mater. Sci. 114 (2016) 135-150 . (Editor’s Choice)
  12. N. Artrith*, W. Sailuam, S. Limpijumnong, and A.M. Kolpak,
    “Reduced Overpotentials for Electrocatalytic Water Splitting over Fe- and Ni-modfied BaTiO3”, Phys. Chem. Chem. Phys. 18 (2016) 29561 .
  13. S. Wannakao, N. Artrith, J. Limtrakul, A.M. Kolpak*,
    “Engineering Transition-Metal-Coated Tungsten Carbides for Efficient and Selective Electrochemical Reduction of CO2 to Methane”, ChemSusChem 8 (2015) 2745.
  14. N. Artrith* and A.M. Kolpak,
    “Grand Canonical Molecular Dynamics Simulations of Cu-Au Nanoalloys in Thermal Equilibrium using Reactive ANN Potentials”, Comput. Mater. Sci. 110 (2015) 20-28 .
  15. N. Artrith* and A.M. Kolpak,
    “Understanding the Composition and Activity of Electrocatalytic Nanoalloys in Aqueous Solvents: A Combination of DFT and Accurate Neural Network Potentials”, Nano Lett. 14 (2014) 2670–2676 .
  16. N. Artrith, B. Hiller, and J. Behler*, “Neural Network Potentials for Metals and Oxides – First Applications to Copper Clusters at Zinc Oxide”, Phys. Status Solidi B 250 (2013) 1191 . ( Feature Article and Front Cover ).
  17. K. V. J. Jose, N. Artrith, and J. Behler*,
    “Construction of High-Dimensional Neural Network Potentials Using Environment-Dependent Atom Pairs”, J. Chem. Phys. 136 (2012) 194111 .
  18. N. Artrith, and J. Behler*,
    “High-Dimensional Neural Network Potentials for Metal Surfaces: A Prototype Study for Copper ”, Phys. Rev. B 85 (2012) 045439 .
  19. N. Artrith, T. Morawietz, and J. Behler*,
    “High-Dimensional Neural-Network Potentials for Multicomponent Systems: Applications to Zinc Oxide ”, Phys. Rev. B 83 (2011) 153101 .
  20. T. Nanok, N. Artrith, P. Pantu, P. A. Bopp, and J. Limtrakul*,
    “Structure and Dynamics of Water Confined in Single-Wall Nanotubes”, J. Phys. Chem. A 113 (2009) 2103-2108 .