Publikationen von Karsten Reuter
Alle Typen
Zeitschriftenartikel (344)
341.
Zeitschriftenartikel
55 (8), S. 5344 - 5352 (1997)
Influence of the data base and algorithmic parameters on the image quality in holographic diffuse LEED. Physical Review B 342.
Zeitschriftenartikel
93 (2), S. 281 - 287 (1997)
A Theoretical Analysis of Ballistic Electron Emission Microscopy: Band Structure Effects and Attenuation Lengths. Acta Physica Polonica A 343.
Zeitschriftenartikel
4 (5), S. 991 - 1001 (1997)
Holographic LEED: A Review of Recent Progress. Surface Review and Letters 344.
Zeitschriftenartikel
54 (11), S. 8172 - 8176 (1996)
Direct reconstruction of three-dimensional atomic adsorption sites by holographic LEED. Physical Review B Buchkapitel (7)
345.
Buchkapitel
Accelerating molecular materials discovery with machine-learning. In: High-Performance Computing and Data Science in the Max Planck Society, S. 40 - 41. Max Planck Computing and Data Facility, Garching (2021)
346.
Buchkapitel
A Decade of Computational Surface Catalysis. In: Handbook of Materials Modeling, 2. Ed. Aufl., S. 1309 - 1319 (Hg. Andreoni, W.; Yip, S.). Springer, Cham (2020)
347.
Buchkapitel
O2 Adsorption Dynamics at Metal Surfaces: Non-adiabatic Effects, Dissociation and Dissipation. In: Dynamics of Gas-Surface Interactions: Atomic-level Understanding of Scattering Processes at Surfaces, S. 389 - 419 (Hg. Díez Muiño, R.; Busnengo, H. F.). Springer, Berlin, Heidelberg (2013)
348.
Buchkapitel
First-principles kinetic Monte Carlo simulations for heterogeneous catalysis: Concepts, status, and frontiers. In: Modeling and Simulation of Heterogeneous Catalytic Reactions: From the Molecular Process to the Technical System, S. 71 - 111 (Hg. Deutschmann, O.). Wiley-VCH, Weinheim (2012)
349.
Buchkapitel
Nanometer and subnanometer thin oxide films at surfaces of late transition metals. In: Nanocatalysis, S. 343 - 376 (Hg. Heiz, U.; Landman, U.). Springer, Berlin (2006)
350.
Buchkapitel
Ab initio atomistic thermodynamics and statistical mechanics of surface properties and functions. In: Handbook of Materials Modeling, S. 149 - 194 (Hg. Yip, S.). Springer, Dordrecht (2005)
351.
Buchkapitel
Theoretische Bausteine zum Verständnis katalytischer Reaktionen an Übergangsmetall-Oxidoberflächen. In: MPG Jahrbuch. Max-Planck-Gesellschaft, Munich (2003)
Konferenzbeitrag (1)
352.
Konferenzbeitrag
Ab initio atomistic thermodynamics for surfaces: A primer. In: Experiment, Modeling and Simulation of Gas-Surface Interactions for Reactive Flows in Hypersonic Flights, S. 2-1 - 2-18. VT-142 RTO AVT/VKI Lecture Series, von Karman Institute, Rhode St. Genèse, Belgium, 06. Februar 2006 - 10. Februar 2006. NATO Research and Technology Organisation, Neuilly-sur-Seine (2007)
Vortrag (92)
353.
Vortrag
Machine Learning Accelerated Materials Discovery for Energy Conversion and Storage. Computation for Applied Catalysis Workshop 2025, Leeds, UK (2025)
354.
Vortrag
Machine Learning Accelerated Materials Discovery for Energy Conversion and Storage. 58. Jahrestreffen Deutscher Katalytiker, Weimar, Germany (2025)
355.
Vortrag
Machine Learning Accelerated Materials Discovery for Energy Conversion and Storage. ACS Spring Meeting 2025, San Diego, CA, USA (2025)
356.
Vortrag
Machine Learning Accelerated Materials Discovery for Energy Conversion and Storage. 4th Annual Ph.D. Workshop, TAming COmplexity in Materials Modeling (TACO), Schladming, Austria (2025)
357.
Vortrag
Machine Learning for Heterogeneous Catalysis and Energy Conversion. AI & Research / MPG event, Berlin, Germany (2024)
358.
Vortrag
Out of the Crystalline Comfort Zone: Tackling Working Interfaces with Machine Learning. CECAM Workshop, Beyond ground state simulations: Navigating challenges in excited states of extended molecules and materials, Lausanne, Switzerland (2024)
359.
Vortrag
Out of the Crystalline Comfort Zone: Tackling Working Interfaces with Machine Learning. Mini-Symposium on Materials and Catalysis: Experiments and Modelling, Barcelona, Spain (2024)
360.
Vortrag
Out of the Crystalline Comfort Zone: Tackling Working Interfaces With Machine Learning. 11th Triennial Congress of the International Society for Theoretical Chemical Physics, Qingdao, China (2024)