Publications 2022 of the
Emeritus Group NOMAD

2022 | 2021 | 2020 | 2019

Journal Article (14)

2022
Journal Article
Aggoune, W., 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).
Journal Article
Boeri, L., 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).
Journal Article
Carbogno, C., 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).
Journal Article
Foppa, L. and L.M. Ghiringhelli: Identifying Outstanding Transition‑Metal‑Alloy Heterogeneous Catalysts for the Oxygen Reduction and Evolution Reactions via Subgroup Discovery. Topics in Catalysis 65 (1-4), 196–206 (2022).
Journal Article
Foppa, L., C.A. 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 (4), 2233–2232 (2022).
Journal Article
Kühbach, M.T., A.J. London, J. Wang, D.K. Schreiber, F. Mendez Martin, I. Ghamarian, H. Bilal and A.V. Ceguerra: Community-Driven Methods for Open and Reproducible Software Tools for Analyzing Datasets from Atom Probe Microscopy. Microscopy and Microanalysis 28 (4), 1038–1053 (2022).
Journal Article
Langer, M.F., 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).
Journal Article
Liu, X., X. Wang, S. Gao, V. Chang, R. Tom, M. Yu, L.M. Ghiringhelli and N. Marom: Finding predictive models for singlet fission by machine learning. npj Computational Materials 8, 70 (2022).
Journal Article
Mazheika, A., Y. Wang, R. Valero, F. Viñes, F. Illas, L.M. Ghiringhelli, S.V. Levchenko and M. Scheffler: Artificial-intelligence-driven discovery of catalyst genes with application to CO2 activation on semiconductor oxides. Nature Communications 13, 419 (2022).
Journal Article
Purcell, T., M. Scheffler, C. Carbogno and L.M. Ghiringhelli: SISSO++: A C++ Implementation of the Sure-Independence Screening and Sparisifying Operator Approach. The Journal of Open Source Software 7 (71), 3960 (2022).
Journal Article
Scheffler, M., 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 (7907), 635–642 (2022).
Journal Article
Tantardini, C., 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).
Journal Article
Zhou, X., Y. Wei, M.T. Kühbach, H. Zhao, F. Vogel, R.D. Kamachali, G.B. Thompson, D. Raabe and B. Gault: Revealing in-plane grain boundary composition features through machine learning from atom probe tomography data. Acta Materialia 226, 117633 (2022).
Journal Article
Zhou, Y., 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 Letters 128 (24), 246101 (2022).

Book (1)

2022
Book
Elsässer, T., M. Grötschel, M. Scheffler, J.H. Ullrich and F.von Blanckenburg: Open Research Data in Naturwissenschaften und Mathematik: Empfehlungen der mathematisch-naturwissenschaftlichen Klasse der BBAW. (Denkanstöße aus der Akademie: Eine Schriftenreihe der Berlin-Brandenburgischen Akademie der Wissenschaften, Vol. 10). Berlin-Brandenburgischen Akademie der Wissenschaften, Berlin (2022).

Thesis - PhD (2)

2022
Thesis - PhD
Leitherer, A.: Robust recognition and exploratory analysis of crystal structures using machine learning. Humboldt Universität Berlin
Thesis - PhD
Lenz, M.-O.: Towards efficient novel materials discovery: Acceleration of high-throughput calculations and semantic management of big data using ontologies. Humboldt-Universität Berlin
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