The NOMAD Laboratory

Novel Materials Discovery at the FHI of the Max-Planck-Gesellschaft
and IRIS-Adlershof of the Humboldt-Universität zu Berlin


Publications of the NOMAD Laboratory

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  1. M. Boley, F. Luong, S. Teshuva, D. F. Schmidt, L. Foppa, M. Scheffler,
    From Prediction to Action: The Critical Role of Proper Performance Estimation for Machine-Learning-Driven Materials Discovery.
    submitted November 27, 2023
    Preprint Download (2023): arXiv

  2. J. Dean, M. Scheffler, T. A. R. Purcell, S. V. Barabash, R. Bhowmik, T. Bazhirov,
    Interpretable Machine Learning for Materials Design.
    Journal of Materials Research (2023)
    Download: pdf

  3. L. Foppa, F. Rüther, M. Geske, G. Koch, F. Girgsdies, P. Kube, S. J. Carey, M. Hävecker, O. Timpe, A. V. Tarasov, M. Scheffler, F. Rosowski, R. Schlögl, and A. Trunschke,
    Data-Centric Heterogeneous Catalysis: Identifying Rules and Materials Genes of Alkane Selective Oxidation.
    J. Am. Chem. Soc. 2023, 145, 6, 3427–3442; https://doi.org/10.1021/jacs.2c11117
    Download: pdf

  4. L. Foppa, M. Scheffler,
    Towards a Multi-Objective Optimization of Subgroups for the Discovery of Materials with Exceptional Performance.
    submitted for publication November 17, 2023, https://doi.org/10.48550/arXiv.2311.10381
    Preprint Download (2023): arXiv

  5. L. M. Ghiringhelli, C. Baldauf, T. Bereau, S. Brockhauser, C. Carbogno, J. Chamanara, S. Cozzini, S. Curtarolo, C. Draxl, S. Dwaraknath, Á. Fekete, J. Kermode, C. T. Koch, M. Kühbach, A. N. Ladines, P. Lambrix, M.-O. Lenz-Himmer, S. Levchenko, M. Oliveira, A. Michalchuk, R. Miller, B. Onat, P. Pavone, G. Pizzi, B. Regler, G.-M. Rignanese, J. Schaarschmidt, M. Scheidgen, A. Schneidewind, T. Sheveleva, C. Su, D. Usvyat, O. Valsson, C. Wöll, and M. Scheffler,
    Shared Metadata for Data-Centric Materials Science.
    Scientific Data 10, 626 (2023); https://doi.org/10.1038/s41597-023-02501-8
    Download: pdf

  6. H. Lu, G. Koknat, Y. Yao, J. Hao, X. Qin, C. Xiao, R. Song, F. Merz, M. Rampp, S. Kokott, C. Carbogno, T. Li, G. Teeter, M. Scheffler, J. J. Berry, D. B. Mitzi, J. L. Blackburn, V. Blum, and M. C. Beard,
    Electronic Impurity Doping of a 2D Hybrid Lead Iodide Perovskite by Bi and Sn.
    PRX Energy 2, 023010 (2023)
    Download: pdf

  7. F. Knoop, T. A. R. Purcell, M. Scheffler, and C. Carbogno,
    Anharmonicity in Thermal Insulators – An Analysis from First Principles.
    Phys. Rev. Lett. 130, 236301 (2023)
    Download: pdf

  8. F. Knoop, M. Scheffler, and C. Carbogno,
    Ab initio Green-Kubo simulations of heat transport in solids: method and implementation.
    Phys. Rev. B 107, 224304 (2023)
    Download: pdf

  9. M. F. Langer, F. Knoop. C. Carbogno, M. Scheffler, and M. Rupp, 
    Heat flux for semi-local machine-learning potentials.
    Phys. Rev. B (Letter) 108, L100302 (2023); https://doi.org/10.1103/PhysRevB.108.L100302
    Download: pdf

  10. M. F. Langer, J. T. Frank, F. Knoop,
    Stress and heat flux via automatic differentiation.
    J. Chem. Phys. 159, 174105 (2023); https://doi.org/10.1063/5.0155760 
    Download: pdf

  11. A. Leitherer, B. C. Yeo, C. H. Liebscher, and L. M. Ghiringhelli,
    Automatic Identification of Crystal Structures and Interfaces via Artificial-Intelligence-based Electron Microscopy.
    npj Computational Materials 9, 17 (2023); https://doi.org/10.1038/s41524-023-01133-1
    Download: pdf

  12. M. Scheidgen, L. Himanen, A. N. Ladines, D. Sikter, M. Nakhaee, Á. Fekete, T. Chang, A. Golparvar, J. A. Márquez, S. Brockhauser, S. Brückner, L. M. Ghiringhelli, F. Dietrich, D. Lehmberg, T. Denell, A. Albino, H. Näsström, S. Shabih, F. Dobener, M. Kühbach, R. Mozumder, J. Rudzinski, N. Daelman, J. M. Pizarro, M. Kuban, P. Ondračka, H.-J. Bungartz, and C. Draxl,
    NOMAD: A distributed web-based platform for managing materials science research data.
    Submitted to JOSS (March 24, 2023)

  13. R. Miyazaki, K. S. Belthle, H. Tüysüz, L. Foppa, M. Scheffler,
    Materials Genes of CO2 Hydrogenation on Supported Cobalt Catalysts: an AI Approach Integrating Theoretical and Experimental Data.
    ChemRxiv. Cambridge: Cambridge Open Engage; 2023; https://doi.org/10.26434/chemrxiv-2023-xx4f1 
    Preprint Download (2023): pdf

  14. O. T. Beynon, A. Owens, C. Carbogno, and A. J. Logsdail,
    Evaluating the Role of Anharmonic Vibrations in Zeolite β Materials.
    J. Phys. Chem. C 127, 16030 (2023)
    Download: pdf 

  15. S. Lu, L. M. Ghiringhelli, C. Carbogno, J. Wang, M. Scheffler,
    On the Uncertainty Estimates of Equivariant-Neural-Network-Ensembles Interatomic Potentials.
    submitted for publication September 1, 2023
    Preprint Download (2023): arXiv

  16. T. A. R. Purcell, M. Scheffler, L. M. Ghiringhelli, C. Carbogno,
    Accelerating Materials-Space Exploration by Mapping Materials Properties via Artificial Intelligence: The Case of the Lattice Thermal Conductivity.
    npj Computational Materials 9 (1), 112 (2023)
    Download: pdf

  17. T. A. R. Purcell, M. Scheffler, L. M. Ghiringhelli,
    Recent advances in the SISSO method and their implementation in the SISSO++ code.
    J. Chem. Phys. 159, 114110 (2023)
    Download (2023): pdf



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