Publications 2022 of the
Emeritus Group Prof. Scheffler

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).

Belthle, K.S., 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. Journal of the American Chemical Society 144 (46), 21232–21243 (2022).

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).

Bowker, M., S. DeBeer, N.F. Dummer, G.J. Hutchings, M. Scheffler, F. Schüth, S.H. Taylor and H. Tüysüz: Advancing Critical Chemical Processes for a Sustainable Future: Challenges for Industry and the Max Planck–Cardiff Centre on the Fundamentals of Heterogeneous Catalysis (FUNCAT). Angewandte Chemie 134 (50), e202209016 (2022).

Bowker, M., S. DeBeer, N.F. Dummer, G.J. Hutchings, M. Scheffler, F. Schüth, S.H. Taylor and H. Tüysüz: Advancing Critical Chemical Processes for a Sustainable Future: Challenges for Industry and the Max Planck–Cardiff Centre on the Fundamentals of Heterogeneous Catalysis (FUNCAT). Angewandte Chemie International Edition 61 (50), e202209016 (2022).

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).

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).

Foppa, L., T. 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 (5), 0545301 (2022).

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).

Hoock, B., S. Rigamonti and C. Draxl: Advancing descriptor search in materials science: feature engineering and selection strategies. New Journal of Physics 24 (11), 113049 (2022).

Huo, H. and M. Rupp: Unified representation of molecules and crystals for machine learning. Machine Learning: Science and Technology 3 (4), 045017 (2022).

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).

Kulik, H.J., T. Hammerschmidt, J. Schmidt, S. Botti, M.A.L. Marques, M. Boley, M. Scheffler, M. Todorović, P. Rinke, C. Oses, A. Smolyanyuk, S. Curtarolo, A. Tkatchenko, A.P. Bartók, S. Manzhos, M. Ihara, T. Carrington, J. Behler, O. Isayev, M. Veit, A. Grisafi, J. Nigam, M. Ceriotti, K.T. Schütt, J. Westermayr, M. Gastegger, R.J. Maurer, B. Kalita, K. Burke, R. Nagai, R. Akashi, O. Sugino, J. Hermann, F. Noé, S. Pilati, C. Draxl, M. Kuban, S. Rigamonti, M. Scheidgen, M. Esters, D. Hicks, C. Toher, P.V. Balachandran, I. Tamblyn, S. Whitelam, C. Bellinger and L.M. Ghiringhelli: Roadmap on Machine learning in electronic structure. Electronic Structure 4 (2), 023004 (2022).

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).

Li, A., P. Nan, Y. Wang, Z. Gao, S. Zhang, Z.-K. Han, X. Zhao, B. Ge, C. Fu and T. Zhu: Chemical stability and degradation mechanism of Mg₃Sb_2-xBi_x thermoelectrics towards room-temperature applications. Acta Materialia 239, 118301 (2022).

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).

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).

Miyazaki, R., K. Iida, S. Ohno, T. Matsuzaki, T. Suzuki, M. Arisawa and J.-ya Hasegawa: Substrate-Assisted Reductive Elimination Determining the Catalytic Cycle: A Theoretical Study on the Ni-Catalyzed 2,3-Disubstituted Benzofuran Synthesis via C-O Bond Activation. Organometallics 41 (23), 3581–3588 (2022).

Moerman, E., F. Hummel, A. Grüneis, A. Irmler and M. Scheffler: Interface to high-performance periodic coupled-cluster theory calculations with atom-centered, localized basis functions. The Journal of Open Source Software 7 (4), 4040 (2022).

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).

Qi, R., B. Zhu, Z.-K. Han and Y. Gao: High-Throughput Screening of Stable Single-Atom Catalysts in CO₂ Reduction Reactions. ACS Catalysis 12 (14), 8269–8278 (2022).

Regler, B., M. Scheffler and L.M. Ghiringhelli: TCMI: a non-parametric mutual-dependence estimator for multivariate continuous distributions. Data Mining and Knowledge Discovery (5), 1815–1864 (2022).

Sbailò, L., 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).

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).

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).

Teale, A.M., 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.D. 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. Xu and W. Yang: DFT exchange: sharing perspectives on the workhorse of quantum chemistry and materials science. Physical Chemistry Chemical Physics 24 (47), 28700–28781 (2022).

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).

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).

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).

Ahmetcik, E.: Artificial intelligence for crystal structure prediction. Technische Universität Berlin

Dragoumi, M.: Quasiparticle energies from second-order perturbation theory. Freie Universität Berlin

Knoop, F.: Heat transport in strongly anharmonic solids from first principles. Humboldt-Universität zu Berlin

Leitherer, A.: Robust recognition and exploratory analysis of crystal structures using machine learning. Humboldt Universität Berlin

Lenz-Himmer, M.-O.: Towards efficient novel materials discovery: Acceleration of high-throughput calculations and semantic management of big data using ontologies. Humboldt-Universität Berlin

Regler, B.: Systematic identification of relevant features for the statistical modeling of materials properties of crystalline solids. Freie Universität Berlin

Yuan, Z.: Electrical conductivity from first principles. Humboldt-Universität zu Berlin

Zhao, B.: Identifying descriptors for the in-silico, high-throughput discovery of the thermal insulators for thermoelectric applications. Technische Universität Darmstadt

Zhu, X.: Ab Initio Green-Kubo Calculations for Strongly Anharmonic Solids: A comparative Benchmark of Lattice Thermal Conductivities. Technische Universität Darmstadt