Publikationen von Luca M. Ghiringhelli

Speckhard, D., C. Carbogno, L.M. Ghiringhelli, S. Lubeck, M. Scheffler und C. Draxl: Extrapolation to the complete basis-set limit in density-functional theory using statistical learning. Physical Review Materials 9 (1), 013801 (2025).

Bauer, S., P. Benner, T. Bereau, V. Blum, M. Boley, C. Carbogno, C.R.A. Catlow, G. Dehm, S. Eibl, R. Ernstorfer, A. Fekete, L. Foppa, P. Fratzl, C. Freysoldt, B. Gault, L.M. Ghiringhelli, S.K. Giri, A. Gladyshev, P. Goyal, J. Hattrick-Simpers, L. Kabalan, P. Karpov, M.S. Khorrami, C. Koch, S. Kokott, T. Kosch, I. Kowalec, K. Kremer, A. Leitherer, Y. Li, C.H. Liebscher, A.J. Logsdail, Z. Lu, F. Luong, A. Marek, F. Merz, J.R. Mianroodi, J. Neugebauer, Z. Pei, T. Purcell, D. Raabe, M. Rampp, M. Rossi, J.-M. Rost, J.E. Saal, U. Saalmann, K.N. Sasidhar, A. Saxena, L. Sbailo, M. Scheidgen, M. Schloz, D.F. Schmidt, S. Teshuva, A. Trunschke, Y. Wei, G. Weikum, R.P. Xian, Y. Yao, J. Yin, M. Zhao und M. Scheffler: Roadmap on data-centric materials science. Modelling and Simulation in Materials Science and Engineering 32 (6), 063301 (2024).

Leitherer, A., B.C. Yeo, C.H. Liebscher und L.M. Ghiringhelli: Automatic identification of crystal structures and interfaces via artificial-intelligence-based electron microscopy. npj Computational Materials 9, 179 (2023).

Purcell, T., M. Scheffler und L.M. Ghiringhelli: Recent advances in the SISSO method and their implementation in the SISSO++ Code. The Journal of Chemical Physics 159 (11), 114110 (2023).

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

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 und M. Scheffler: Numerical quality control for DFT-based materials databases. npj Computational Materials 8, 69 (2022).

Foppa, L. und 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. und L.M. Ghiringhelli: Correction to: Identifying Outstanding Transition-Metal-Alloy Heterogeneous Catalysts for the Oxygen Reduction and Evolution Reactions via Subgroup Discovery. Topics in Catalysis 65 (1-4), 207–207 (2022).

Foppa, L., T. Purcell, S.V. Levchenko, M. Scheffler und 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 und M. Scheffler: Learning Design Rules for Selective Oxidation Catalysts from High-Throughput Experimentation and Artificial Intelligence. ACS Catalysis 12 (4), 2233–2232 (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 und L.M. Ghiringhelli: Roadmap on Machine learning in electronic structure. Electronic Structure 4 (2), 023004 (2022).

Liu, X., X. Wang, S. Gao, V. Chang, R. Tom, M. Yu, L.M. Ghiringhelli und 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 und M. Scheffler: Artificial-intelligence-driven discovery of catalyst genes with application to CO₂ activation on semiconductor oxides. Nature Communications 13, 419 (2022).

Purcell, T., M. Scheffler, C. Carbogno und 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).

Regler, B., M. Scheffler und 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 und M. Scheffler: The NOMAD Artificial-Intelligence Toolkit: Turning materials-science data into knowledge and understanding. npj Computational Materials 8, 250 (2022).

Zhou, Y., C. Zhu, M. Scheffler und 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).

Dutta, A., J. Vreeken, L.M. Ghiringhelli und T. Bereau: Data-driven equation for drug-membrane permeability across drugs and membranes. The Journal of Chemical Physics 154 (24), 244114 (2021).

Dutta, A., J. Vreeken, L.M. Ghiringhelli und T. Bereau: Publisher's Note: "Data-driven equation for drug-membrane permeability across drugs and membranes" [J. J. chem. Phys. 154, 244114 (2021)]. The Journal of Chemical Physics 155 (3), 039901 (2021).

Foppa, L., L.M. Ghiringhelli, F. Girgsdies, M. Hashagen, P. Kube, M. Hävecker, S. Carey, A. Tarasov, P. Kraus, F. Rosowski, R. Schlögl, A. Trunschke und M. Scheffler: Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence. MRS Bulletin 46 (11), 1016–1026 (2021).