Publications of Matthias Scheffler

Lorenz, S.: Reactions on surfaces with neural networks. Technische Universität Berlin

Seitsonen, A.P.: Theoretical investigations into adsorption and co-adsorption on transition-metal surfaces as models to heterogenous catalysis. Technische Universität Berlin

Petersen, M.: Dichtefunktionaltheoretische Untersuchung zur Wechselwirkung von H, He und Ne mit Metalloberflächen. Technische Universität Berlin

Scheffler, M.: Winkelaufgelöste Photoemission von adsorbierten Schichten. Technische Universität Berlin

Scheffler, M.: Winkelaufgelöste Photoemission von Adsorbatsystemen. Technische Universität Berlin Berlin

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

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

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

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

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

Ahmetcik, E.: Machine Learning of the Stability of Octet Binaries. Technische Universität Berlin

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

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

Sugathan Nair, A., L. Foppa and M. Scheffler: Materials-Discovery Workflows Guided by Symbolic Regression: Identifying Acid-Stable Oxides for Electrocatalysis., in press.

Behler, J., G. Csanyi, L. Foppa, K. Kang, M.F. Langer, J.T. Margraf, A. Sugathan Nair, T.A.R. Purcell, P. Rinke, M. Scheffler, A. Tkatchenko, M. Todorovic, O.T. Unke and Y. Yao: Workflows for Artificial Intelligence., in press.

Quan, J., M.-Y. Zhang, M. Scheffler and C. Carbogno: Temperature-Dependent Electronic Spectral Functions From Band-Structure Unfolding., in press.

Moerman, E. and M. Scheffler: Coupled-Cluster Theory for the Ground State and for Excitations., in press.

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

Foppa, L. and M. Scheffler: Coherent Collections of Rules Describing Exceptional Materials Identified with a Multi-Objective Optimization of Subgroups., in press.

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