FHI
The NOMAD Laboratory

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

Publications

2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990 Before1990

2022

Articles

  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.
    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); https://doi.org/10.1088/1361-648X/ac2864
    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); https://doi.org/10.1038/s41524-022-00744-4
    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
    https://doi.org/21.11116/0000-000A-CFFA-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); https://doi.org/10.1103/PhysRevLett.129.055301
    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); https://doi.org/10.1021/acscatal.1c04793
    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.
    (submitted)
    Preprint Download: arXiv
  13. F. Knoop, M. Scheffler, and C. Carbogno,
    Ab initio Green-Kubo simulations of heat transport in solids: method and implementation.
    (submitted)
    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); https://doi.org/10.1038/s41467-022-28042-z
    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); https://doi.org/10.21105/joss.04040
    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); https://doi.org/10.21105/joss.03960
    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)
    https://doi.org/10.1007/s10618-022-00847-y
    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); https://www.doi.org/10.1038/s41586-022-04501-x
    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. https://doi.org/10.1039/D2CP02827A
    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);  https://doi.org/10.1103/PhysRevLett.128.246101
    Download: pdf

  23. 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
  24. F. Knoop,
    Heat transport in strongly anharmonic solids from first principles.
    HU Berlin, 2022; https://doi.org/10.18452/24244
    Reprint download: pdf
  25. E. Ahmetik,
    Artificial Intelligence for Crystal Structure Prediction.
    TU Berlin, 2022; https://doi.org/10.14279/depositonce-16033
    Reprint download: pdf
  26. Z. Yuan,
    Electrical conductivity from first principles.
    HU Berlin, 2022.
    Reprint download: pdf
  27. 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
  28. B. Zhao,
    Identifying descriptors for the In-silico, high-throughput discovery of the thermal Insulators for thermoelectric applications.
    TU Darmstadt, 2022.
    Reprint download: pdf
  29. X. Zhu,
    Ab Initio green-kubo calculations for strongly anharmonic solids: a comparative benchmark of lattice thermal conductivities.
    TU Darmstadt, 2022
    Reprint 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
  6. B. Zhao,
    Identifying descriptors for the In-silico, high-throughput discovery of the thermal Insulators for thermoelectric applications.
    TU Darmstadt, 2022.
    Reprint download: pdf
  7. X. Zhu,
    Ab Initio green-kubo calculations for strongly anharmonic solids: a comparative benchmark of lattice thermal conductivities.
    TU Darmstadt, 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

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