Publications in 2022


  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);
    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.
    Modelling and Simulation in Materials Science and Engineering 30 (2022).
    Preprint Download: arXiv
  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);
    Download: pdf
  4. M. Boley and M. Scheffler,
    Learning Rules for Materials Properties and Functions.
    Roadmap for Machine Learning in Electronic Structure Theory, ed. by Silvana Botti and Miguel Marques
    Preprint Download: arXiv
  5. 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, npj Computational Materials 8, 69 (2022);
    Download: pdf
  6. J. Dean, M. Scheffler, T. A. R. Purcell, S. V. Barabash, R. Bhowmik, T. Bazhirov,
    Interpretable Machine Learning for Materials Design.
    Preprint Download: arXiv
  7. 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
  8. 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);
    Download: pdf
  9. 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);
    Download: ACS Publications
  10. 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.
    Submitted to Scientific Data on May 29, 2022.
    Preprint Download: arXiv
  11. L. M. Ghiringhelli,
    Interpretability of machine-learning models in physical sciences.
    Roadmap for Machine Learning in Electronic Structure Theory, ed. by Silvana Botti and Miguel Marques
    Preprint Download: arXiv
  12. F. Knoop, T.A.R. Purcell, M. Scheffler, and C. Carbogno,
    Anharmonicity in Thermal Insulators – An Analysis from First Principles.
    Preprint Download: arXiv
  13. F. Knoop, M. Scheffler, and C. Carbogno,
    Ab initio Green-Kubo simulations of heat transport in solids: method and implementation.
    Preprint Download: arXiv
  14. 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);
    Download: pdf
  15. 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.
    Submitted to Journal of Open Source Software (JOSS) (2022);
    Preprint Download: arXiv
  16. T. 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);
    Download: pdf
  17. T. 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.
    Preprint Download: arXiv
  18. B. Regler, M. Scheffler, and L.M. Ghiringhelli,
    TCMI: a non-parametric mutual-dependence estimator for multivariate continuous distributions. Submitted to Data Mining and Knowledge Discovery (Jan 30, 2020)
    Download: pdf
  19. 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);
    Reprint Download: pdf
    Preprint Download: arXiv
  20. 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: sciencedirect
  21. 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.
    Physical Chemistry Chemical Physics (2022), in print.
    Download: pdf
  22. 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);
    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;
    Download: pdf
    Reprint download: pdf
  2. F. Knoop,
    Heat transport in strongly anharmonic solids from first principles.
    HU Berlin, 2022;
    Reprint download: pdf
  3. E. Ahmetik,
    Artificial Intelligence for Crystal Structure Prediction.
    TU Berlin, 2022;
    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;
    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


Page last modified on November 22, 2022, at 10:09 AM CET