Publications of Luca M. Ghiringhelli

Speckhard, D., C. Carbogno, L.M. Ghiringhelli, S. Lubeck, M. Scheffler and 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 and 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 and 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 and 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 and 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 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).

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).

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).

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).

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).

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).

Dutta, A., J. Vreeken, L.M. Ghiringhelli and 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 and 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 and M. Scheffler: Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence. MRS Bulletin 46 (11), 1016–1026 (2021).

Leitherer, A., A. Ziletti and L.M. Ghiringhelli: Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning. Nature Communications 12 (1), 6234 (2021).