Publikationen von Yonghyuk Lee

Lee, Y., J. Timmermann, C. Panosetti, C. Scheurer und K. Reuter: Staged Training of Machine-Learning Potentials from Small to Large Surface Unit Cells: Efficient Global Structure Determination of the RuO₂(100)-c(2 × 2) Reconstruction and (410) Vicinal. The Journal of Physical Chemistry C 127 (35), 17599–17608 (2023).

Panosetti, C., Y. Lee, A. Samtsevych und C. Scheurer: Black box vs gray box: Comparing GAP and GPrep-DFTB for ruthenium and ruthenium oxide. The Journal of Chemical Physics 158 (22), 224115 (2023).

Yoo, S.-H., L.S. Aota, S. Shin, A.A. El-Zoka, P.W. Kang, Y. Lee, H. Lee, S.-H. Kim und B. Gault: Dopant Evolution in Electrocatalysts after Hydrogen Oxidation Reaction in an Alkaline Environment. ACS Energy Letters 8 (8), 3381–3386 (2023).

Lee, Y., C. Scheurer und K. Reuter: Epitaxial Core-Shell Oxide Nanoparticles: First-Principles Evidence for Increased Activity and Stability of Rutile Catalysts for Acidic Oxygen Evolution. ChemSusChem 15 (10), e202200015 (2022).

Timmermann, J., Y. Lee, C. Staacke, J. Margraf, C. Scheurer und K. Reuter: Data-Efficient Iterative Training of Gaussian Approximation Potentials: Application to Surface Structure Determination of Rutile IrO₂ and RuO₂. The Journal of Chemical Physics 155 (24), 244107 (2021).

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