Publikationen von Matthias Scheffler

Cao, J.: Polymerization of Carbon Nitride - experimental and theoretical investigation. Diplom, Freie Universität, Berlin (2006)

Zhao, B.: Identifying descriptors for the in-silico, high-throughput discovery of the thermal insulators for thermoelectric applications. Master, Technische Universität, Darmstadt (2022)

Lim, B.: Discussion, implementation and demonstration of AI-guided active workflows. Master, Technische Universität, Darmstadt (2021)

Oehlers, M.: Identifying exceptional data points in materials science using machine learning. Master, Technische Universität, Berlin (2021)

Kowalski, H.-H.: First-principles Study of Thermoelectric Magnesium Silicides with High-Throughput Techniques. Master, Technische Universität, Berlin (2016)

Ahmetcik, E.: Machine Learning of the Stability of Octet Binaries. Master, Technische Universität, Berlin (2016)

Müller, P. M.: Thermal Conductivities of Group IV and Group III-V Compound Semiconductors from First Principles. Bachelor, Technische Universität, Berlin (2018)

Mauß, J. M.; Kley, K. S.; Khobragade, R.; Tran, N. K.; De Bellis, J.; Schüth, F.; Scheffler, M.; Foppa, L.: Modelling the Time-Dependent Reactivity of Catalysts by Experiments and Artificial Intelligence. (2025)

Sugathan Nair, A.; Foppa, L.; Scheffler, M.: Materials-Discovery Workflows Guided by Symbolic Regression: Identifying Acid-Stable Oxides for Electrocatalysis. (2024)

Behler, J.; Csanyi, G.; Foppa, L.; Kang, K.; Langer, M. F.; Margraf, J. T.; Sugathan Nair, A.; Purcell, T. A. R.; Rinke, P.; Scheffler, M. et al.; Tkatchenko, A.; Todorovic, M.; Unke, O. T.; Yao, Y.: Workflows for Artificial Intelligence. (2024)

Quan, J.; Zhang, M.-Y.; Scheffler, M.; Carbogno, C.: Temperature-Dependent Electronic Spectral Functions From Band-Structure Unfolding. (2024)

Moerman, E.; Scheffler, M.: Coupled-Cluster Theory for the Ground State and for Excitations. (2024)

Kang, K.; Scheffler, M.; Carbogno, C.; Purcell, T.: Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials Through Active Learning. (2024)

Foppa, L.; Scheffler, M.: Coherent Collections of Rules Describing Exceptional Materials Identified with a Multi-Objective Optimization of Subgroups. (2024)

Boley, M.; Luong, F.; Teshuva, S.; Schmidt, D. F.; Foppa, L.; Scheffler, M.: From Prediction to Action: The Critical Role of Proper Performance Estimation for Machine-Learning-Driven Materials Discovery. (2023)

Foppa, L.; Scheffler, M.: Towards a Multi-Objective Optimization of Subgroups for the Discovery of Materials with Exceptional Performance. (2023)

Lu, S.; Ghiringhelli, L. M.; Carbogno, C.; Wang, J.; Scheffler, M.: On the Uncertainty Estimates of Equivariant-Neural-Network-Ensembles Interatomic Potentials. (2023)

Speckhard, D.; Carbogno, C.; Ghiringhelli, L. M.; Lubeck, S.; Scheffler, M.; Draxl, C.: Extrapolation to complete basis-set limit in density-functional theory by quantile random-forest models. (2023)

Boley, M.; Scheffler, M.: Learning Rules for Materials Properties and Functions. (2021)

Mazheika, A.; Wang, Y.; Valero, R.; Ghiringhelli, L. M.; Vines, F.; Illas, F.; Levchenko, S. V.; Scheffler, M.: Ab initio data-analytics study of carbon-dioxide activation on semiconductor oxide surfaces. (2019)