Publikationen von Karsten Reuter

Reuter, K.: Knowledge-Based Approaches in Catalysis and Energy Modelling. 66th Symposium of the American Vacuum Society, Columbus, OH, USA (2019)

Reuter, K.: Multiscale Theory of Operando Energy Conversion Systems. Seminar, Thomas Young Center (TYC), Imperial College, London, UK (2019)

Reuter, K.: Knowledge-Based Approaches in Catalysis and Energy Modelling. Catalysis Summit, Filzmoos, Austria (2019)

Reuter, K.: Multiscale Theory of Operando Energy Conversion Systems. Workshop, Hands-On DFT and Beyond, High-throughput screening and big-data analytics, towards exascale computational materials science, Barcelona, Spain (2019)

Oschinski, H.: Efficient Modeling of Interfacial Capacitance Effects in Computational Electrochemistry. Dissertation, Technische Universität, Berlin (2025)

Dudzinski, A.: First Step of the Oxygen Reduction Reaction: A Computational Study of O₂ Adsorption at Electrified Metal/Water Interfaces. Dissertation, Freie Universität, Berlin (2025)

Eggert, T.: Structure and Energetics of Metal-Water Interfaces from Atomistic Simulations. Dissertation, Technische Universität, München (2024)

Landini, E.: Machine Learning Based Screening of Double Perovskites for Photovoltaic Applications. Dissertation, Technische Universität, München (2023)

Chen, K.: Molecular Machine Learning for Complex Electronic Properties. Dissertation, Technische Universität, München (2023)

Ghan, S.: Electronic Couplings from Density Functional Theory: Quantitative Approaches. Dissertation, Technische Universität, München (2023)

Annies, S.: Meso-scale modeling of the structural, electronic and transport properties governing (dis-)charging processes in lithium intercalated graphite anodes. Dissertation, Technische Universität, München (2023)

Deimel, M.: Ab Initio Studies of the Activity and Selectivity of Transition Metal Catalysts for CO Hydrogenation. Dissertation, Technische Universität, München (2023)

Türk, H.: Active Site Generation and Deactivation of the Air Electrode in High Temperature Solid Oxide Cells. Dissertation, Technische Universität, München (2022)

Lee, Y.: Discoveries in Ruthenium Oxide-Based Catalysts: From Morphology Control for Water Electrolysis to Surface Structure Determination via Machine-Learning. Dissertation, Technische Universität, München (2022)

Xu, W.: Tailoring complexity for catalyst discovery using physically motivated machine learning. Dissertation, Technische Universität, München (2022)

Staacke, C.: The Electrostatic Gap: Combining Electrostatic Models with Machine Learning Potentials. Dissertation, Technische Universität, München (2022)

Stegmaier, S.: Working Interfaces in Solid-State Electrolytes: A Theoretical Approach towards Realistic Models. Dissertation, Technische Universität, München (2022)

Stocker, S.: Transferability in chemical machine learning. Dissertation, Technische Universität, München (2022)

Wengert, S.: Kernel-based machine learning for molecular crystal structure prediction. Dissertation, Technische Universität, München (2022)

Grosu, C.: Capturing ion dynamics in lithium intercalated graphite: bridging the gap between experiment and theory through advanced nuclear magnetic resonance and multiscale modeling. Dissertation, Technische Universität, München (2022)