Publications of Matthias Scheffler

Langer, M. F.; Knoop, F.; Carbogno, C.; Scheffler, M.; Rupp, M.: Heat flux for semilocal machine-learning potentials. Physical Review B 108 (10), L100302 (2023)

Purcell, T.; Scheffler, M.; Ghiringhelli, L. M.; Carbogno, C.: Accelerating materials-space exploration for thermal insulators by mapping materials properties via artificial intelligence. npj Computational Materials 9, 112 (2023)

Knoop, F.; Purcell, T.; Scheffler, M.; Carbogno, C.: Anharmonicity in Thermal Insulators: An Analysis from First Principles. Physical Review Letters 130 (23), 236301 (2023)

Lu, H.; Koknat, G.; Yao, Y.; Hao, J.; Qin, X.; Xiao, C.; Song, R.; Merz, F.; Rampp, M.; Kokott, S. et al.; Carbogno, C.; Li, T.; Teeter, G.; Scheffler, M.; Berry, J. J.; Mitzi, D. B.; Blackburn, J. L.; Blum, V.; Beard, M. C.: Electronic Impurity Doping of a 2D Hybrid Lead Iodide Perovskite by Bi and Sn. PRX Energy 2 (2), 023010 (2023)

Knoop, F.; Scheffler, M.; Carbogno, C.: Ab initio Green-Kubo simulations of heat transport in solids: Method and implementation. Physical Review B 107 (22), 224304 (2023)

Foppa, L.; Rüther, F.; Geske, M.; Koch, G.; Girgsdies, F.; Kube, P.; Carey, S.; Hävecker, M.; Timpe, O.; Tarasov, A. et al.; Scheffler, M.; Rosowski, F.; Schlögl, R.; Trunschke, A.: Data-Centric Heterogeneous Catalysis: Identifying Rules and Materials Genes of Alkane Selective Oxidation. Journal of the American Chemical Society 145 (6), pp. 3427 - 3442 (2023)

Teale, A. M.; Helgaker, T.; Savin, A.; Adamo, C.; Aradi, B.; Arbuznikov, A. V.; Ayers, P. W.; Baerends, E. J.; Barone, V.; Calaminici, P. et al.; Cancès, E.; Carter, E. A.; Chattaraj, P. K.; Chermette, H.; Ciofini, I.; Crawford, T. D.; Proft, F. D.; Dobson, J. F.; Draxl, C.; Frauenheim, T.; Fromager, E.; Fuentealba, P.; Gagliardi, L.; Galli, G.; Gao, J.; Geerlings, P.; Gidopoulos, N.; Gill, P. M. W.; Gori-Giorgi, P.; Görling, A.; Gould, T.; Grimme, S.; Gritsenko, O.; Jensen, H. J. A.; Johnson, E. R.; Jones, R. O.; Kaupp, M.; Köster, A. M.; Kronik, L.; Krylov, A. I.; Kvaal, S.; Laestadius, A.; Levy, M.; Lewin, M.; Liu, S.; Loos, P.-F.; Maitra, N. T.; Neese, F.; Perdew, J. P.; Pernal, K.; Pernot, P.; Piecuch, P.; Rebolini, E.; Reining, L.; Romaniello, P.; Ruzsinszky, A.; Salahub, D. R.; Scheffler, M.; Schwerdtfeger, P.; Staroverov, V. N.; Sun, J.; Tellgren, E.; Tozer, D. J.; Trickey, S. B.; Ullrich, C. A.; Vela, A.; Vignale, G.; Wesolowski, T. A.; Xu, X.; Yang, W.: DFT exchange: sharing perspectives on the workhorse of quantum chemistry and materials science. Physical Chemistry Chemical Physics 24 (47), pp. 28700 - 28781 (2022)

Bowker, M.; DeBeer, S.; Dummer, N. F.; Hutchings, G. J.; Scheffler, M.; Schüth, F.; Taylor, S. H.; Tüysüz, H.: 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)

Bowker, M.; DeBeer, S.; Dummer, N. F.; Hutchings, G. J.; Scheffler, M.; Schüth, F.; Taylor, S. H.; Tüysüz, H.: 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)

Sbailò, L.; Fekete, Á.; Ghiringhelli, L. M.; Scheffler, M.: The NOMAD Artificial-Intelligence Toolkit: Turning materials-science data into knowledge and understanding. npj Computational Materials 8, 250 (2022)

Regler, B.; Scheffler, M.; Ghiringhelli, L. M.: TCMI: a non-parametric mutual-dependence estimator for multivariate continuous distributions. Data Mining and Knowledge Discovery 36 (5), pp. 1815 - 1864 (2022)

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

Foppa, L.; Purcell, T.; Levchenko, S. V.; Scheffler, M.; Ghiringhelli, L. M.: Hierarchical Symbolic Regression for Identifying Key Physical Parameters Correlated with Bulk Properties of Perovskites. Physical Review Letters 129 (5), 0545301 (2022)

Moerman, E.; Hummel, F.; Grüneis, A.; Irmler, A.; Scheffler, M.: 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)

Zhou, Y.; Zhu, C.; Scheffler, M.; Ghiringhelli, L. M.: 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)

Scheffler, M.; Aeschlimann, M.; Albrecht, M.; Bereau, T.; Bungartz, H.-J.; Felser, C.; Greiner, M.; Groß, A.; Koch, C. T.; Kremer, K. et al.; Nagel, W. E.; Scheidgen, M.; Wöll, C.; Draxl, C.: FAIR data enabling new horizons for materials research. Nature 604 (7907), pp. 635 - 642 (2022)

Carbogno, C.; Thygesen, K. S.; Bieniek, B.; Draxl, C.; Ghiringhelli, L. M.; Gulans, A.; Hofmann, O. T.; Jacobsen, K. W.; Lubeck, S.; Mortensen, J. J. et al.; Strange, M.; Wruss, E.; Scheffler, M.: Numerical quality control for DFT-based materials databases. npj Computational Materials 8, 69 (2022)

Purcell, T.; Scheffler, M.; Carbogno, C.; Ghiringhelli, L. M.: SISSO++: A C++ Implementation of the Sure-Independence Screening and Sparisifying Operator Approach. The Journal of Open Source Software 7 (71), 3960 (2022)

Foppa, L.; Sutton, C. A.; Ghiringhelli, L. M.; De, S.; Löser, P.; Schunk, S. A.; Schäfer, A.; Scheffler, M.: Learning Design Rules for Selective Oxidation Catalysts from High-Throughput Experimentation and Artificial Intelligence. ACS Catalysis 12 (4), pp. 2233 - 2232 (2022)

Mazheika, A.; Wang, Y.; Valero, R.; Viñes, F.; Illas, F.; Ghiringhelli, L. M.; Levchenko, S. V.; Scheffler, M.: Artificial-intelligence-driven discovery of catalyst genes with application to CO₂ activation on semiconductor oxides. Nature Communications 13, 419 (2022)