The NOMAD Laboratory

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




  1. W. Aggoune, A. Eljarrat, D. Nabok, K. Irmscher, M. Zupancic, Z. Galazka, M. Albrecht, C. Koch and C. Draxl,
    A consistent picture of excitations in cubic BaSnO3 revealed by combining theory and experiment.
    Communications Materials 3, 12 (2022); https://doi.org/10.1088/1361-648X/ac2864
    Download: pdf
  2. V. Blum, M. Rossi, S. Kokott, and M. Scheffler,
    The FHI-aims Code: All-electron, ab initio materials simulations towards the exascale.
    Roadmap on electronic structure codes in the exascale era,
    Vikram Gavini et al 2023 Modelling Simul. Mater. Sci. Eng. 31 063301; DOI 10.1088/1361-651X/acdf06
    Download: pdf

  3. L. Boeri, R.G. Hennig, P.J. Hirschfeld, G. Profeta, A. Sanna, E. Zurek, W.E. Pickett, M. Amsler, R. Dias, M. Eremets, C. Heil, R. Hemley, H. Liu, Y. Ma, C. Pierleoni, A. Kolmogorov, N. Rybin, D. Novoselov, V.I. Anisimov, A.R. Oganov, C.J. Pickard, T. Bi, R. Arita, I. Errea, C. Pellegrini, R. Requist, E.K.U. Gross, E.R. Margine, S.R. Xie, Y. Quan, A. Hire, L. Fanfarillo, G.R. Stewart, J.J. Hamlin, V. Stanev, R.S. Gonnelli, E. Piatti, D. Romanin, D. Daghero and R. Valenti,
    The 2021 Room-Temperature Superconductivity Roadmap.
    Journal of Physics: Condensed Matter 34 (18), 183002 (2022); https://doi.org/10.1088/1361-648X/ac2864
    Download: pdf
  4. C. Carbogno, K.S. Thygesen, B. Bieniek, C. Draxl, L.M. Ghiringhelli, A. Gulans, O. T. Hofmann, K. W. Jacobsen, S. Lubeck, J. J. Mortensen, M. Strange, E. Wruss, and M. Scheffler,
    Numerical Quality Control for DFT-based Materials Databases.
    npj Computational Materials 8, 69 (2022); https://doi.org/10.1038/s41524-022-00744-4
    Download: pdf

  5. M. Boley and M. Scheffler,
    Learning Rules for Materials Properties and Functions.
    Roadmap on Machine learning in electronic structure,
    Electron. Struct. 4, 023004 (2022); DOI 10.1088/2516-1075/ac572f
    Download: pdf

  6. T. Elsaesser, M. Groetschel, M. Scheffler, J. H. Ullrich, F. von Blanckenburg
    Open Research Data in Naturwissenschaften und Mathematik.
    Empfehlungen der Mathematisch-Naturwissenschaftlichen Klasse der BBAW, ed. by: Der Praesident der Berlin-Brandenburgischen Akademie der Wissenschaften, ISBN:978-3-949455-12-4
    Download: pdf
  7. L. Foppa, T. A. R. Purcell, S. V. Levchenko, M. Scheffler, and L. M. Ghiringhelli,
    Hierarchical symbolic regression for identifying key physical parameters correlated with bulk properties of perovskites .
    Physical Review Letters 129, 55301 (2022); https://doi.org/10.1103/PhysRevLett.129.055301
    Download: pdf
  8. L. Foppa, C. Sutton, L. M. Ghiringhelli, S. De, P. Löser, S.A. Schunk, A. Schäfer, and M. Scheffler,
    Learning design rules for selective oxidation catalysts from high-throughput experimentation and artificial intelligence.
    ACS Catalysis 12, 2223 (2022); https://doi.org/10.1021/acscatal.1c04793
    Download: ACS Publications
  9. A. Mazheika, Y. Wang, R. Valero, F. Vines, F. Illas, L. Ghiringhelli, S. Levchenko, and M. Scheffler,
    Artificial-intelligence-driven discovery of catalyst “genes” with application to CO2 activation on semiconductor oxides.
    Nature Communications 13, 419 (2022); https://doi.org/10.1038/s41467-022-28042-z
    Download: pdf

  10. E. Moerman, F. Hummel, A. Grüneis, A. Irmler, M. Scheffler,
    Interface to high-performance periodic coupled-cluster theory calculations with atom-centered, localized basis functions.
    Journal of Open Source Software, 7 (74), 4040; https://doi.org/10.21105/joss.04040
    Download: pdf

  11. L. M. Ghiringhelli,
    Interpretability of machine-learning models in physical sciences.
    Roadmap on Machine learning in electronic structure, ed. by Silvana Botti and Miguel Marques,
    H J Kulik et al 2022 Electron. Struct. 4 023004, https://doi.org/10.1088/2516-1075/ac572f 
    Download: pdf

  12. T. A. R. Purcell, M. Scheffler, C. Carbogno, and L.M. Ghiringhelli,
    SISSO++: A C++ Implementation of the Sure-Independence Screening and Sparsifying Operator Approach.
    Journal of Open Source Software 7 (71), 3960 (2022); https://doi.org/10.21105/joss.03960
    Download: pdf

  13. B. Regler, M. Scheffler, and L.M. Ghiringhelli,
    TCMI: a non-parametric mutual-dependence estimator for multivariate continuous distributions. Data Min Knowl Disc 36, 1815–1864 (2022); https://doi.org/10.1007/s10618-022-00847-y
    Download: pdf

  14. L. Sbailò, Á. Fekete, L. M. Ghiringhelli, and M. Scheffler
    The NOMAD Artificial-Intelligence Toolkit: turning materials-science data into knowledge and understanding.
    npj Computational Materials 8, 250 (2022); https://doi.org/10.1038/s41524-022-00935-z
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  15. M. Scheffler, M. Aeschlimann, M. Albrecht, T. Bereau, H.-J. Bungartz, C. Felser, M. Greiner, A. Groß, C. T. Koch, K. Kremer, W. E. Nagel, M. Scheidgen, C. Wöll, and C. Draxl,
    FAIR data enabling new horizons for materials research.
    Nature 604, 635 (2022); https://www.doi.org/10.1038/s41586-022-04501-x
    Download: pdf

  16. C. Tantardini, S. Kokott, X. Gonze, S.V. Levchenko and W.A. Saidi,
    “Self-trapping” in solar cell hybrid inorganic-organic perovskite absorbers.
    Applied Materials Today 26, 101380 (2022).
    Download: pdf

  17. A. M. Teale, T. Helgaker, A. Savin, C. Adamo, B. Aradi, A. V. Arbuznikov, P. W. Ayers, E. J. Baerends, V. Barone, P. Calaminici, E. Cancès, E. A. Carter, P. K. Chattaraj, H. Chermette, I. Ciofini, T. D. Crawford, F. De Proft, J. F. Dobson, C. Draxl, T. Frauenheim, E. Fromager, P. Fuentealba, L. Gagliardi, G. Galli, J. Gao, P. Geerlings, N. Gidopoulos, P. M. W. Gill, P. Gori-Giorgi, A. Görling, T. Gould, S. Grimme, O. Gritsenko, H. J. A.Jensen, E. R. Johnson, R. O. Jones, M. Kaupp, A. M. Köster, L. Kronik, A. I. Krylov, S. Kvaal, A. Laestadius, M. Levy, M. Lewin, S. Liu, P.-F. Loos, N. T. Maitra, F. Neese, J. P. Perdew, K. Pernal, P. Pernot, P. Piecuch, E. Rebolini, L. Reining, P. Romaniello, A. Ruzsinszky, D. R. Salahub, M. Scheffler, P. Schwerdtfeger, V. N. Staroverov, J. Sun, E. Tellgren, D. J. Tozer, S. B. Trickey, C. A. Ullrich, A. Vela, G. Vignale, T. A. Wesolowski, X. W. Yang,
    DFT Exchange: Sharing Perspectives on the Workhorse of Quantum Chemistry and Materials Science.
    Phys. Chem. Chem. Phys. 24, 28700-28781 (2022); https://doi.org/10.1039/D2CP02827A
    Download: pdf

  18. Y. Zhou, C. Zhu, M. Scheffler, and L. M. Ghiringhelli,
    Ab initio approach for thermodynamic surface phases with full consideration of anharmonic effects – the example of hydrogen at Si(100).
    Physical Review Letter 128, 246101 (2022);  https:/doi.org/10.1103/PhysRevLett.128.246101
    Download: pdf

Ph.D. Thesis

  1. M.O. Lenz-Himmer,
    Towards Efficient Novel Materials Discovery Acceleration of High-throughput Calculations and Semantic Management of Big Data using Ontologies.
    HU Berlin, 2022; https://doi.org/10.18452/24340
    Download: pdf
    Reprint download: pdf
  2. F. Knoop,
    Heat transport in strongly anharmonic solids from first principles.
    HU Berlin, 2022; https://doi.org/10.18452/24244
    Reprint download: pdf
  3. E. Ahmetik,
    Artificial Intelligence for Crystal Structure Prediction.
    TU Berlin, 2022; https://doi.org/10.14279/depositonce-16033
    Reprint download: pdf
  4. Z. Yuan,
    Electrical conductivity from first principles.
    HU Berlin, 2022.
    Reprint download: pdf
  5. B. Regler,
    Systematic identification of relevant features for the statistical modeling of materials properties of crystalline solids.
    FU Berlin, 2022; http://dx.doi.org/10.17169/refubium-35222
    Reprint download: pdf

Master Thesis

  1. B. Zhao,
    Identifying descriptors for the In-silico, high-throughput discovery of the thermal Insulators for thermoelectric applications.
    TU Darmstadt, 2022.
    Reprint download: pdf
  2. X. Zhu,
    Ab Initio green-kubo calculations for strongly anharmonic solids: a comparative benchmark of lattice thermal conductivities.
    TU Darmstadt, 2022
    Reprint download: pdf


  1. M. F. Langer, A. Goeßmann, and M. Rupp,
    Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning.
    npj Computational Materials 8, 41 (2022); https://doi.org/10.1038/s41524-022-00721-x
    Download: pdf

Ph.D. Thesis

  1. M. Dragoumi,
    Quasiparticle energies from second-order perturbation theory
    FU Berlin, 2022;
    Download: pdf


  1. K. S. Belthle, T. Beyazay, C. Ochoa-Hernández, R. Miyazaki, L. Foppa, W. F. Martin, and H. Tüysüz,
    Effects of Silica Modification (Mg, Al, Ca, Ti, and Zr) on Supported Cobalt Catalysts for H2-Dependent CO2 Reduction to Metabolic Intermediates.
    J. Am. Chem. Soc. 2022, 144, 46, 21232–21243; https://doi.org/10.1021/jacs.2c08845
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