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O: Fachverband Oberflächenphysik
O 9: Surface Reactions
O 9.8: Vortrag
Montag, 17. März 2025, 12:15–12:30, H25
Enhanced Sampling of Chiral Molecules on Chiral PdGa Surfaces Using Machine Learning — •Raymond Christopher Amador1,2, Umberto Raucci3, Peilin Kang3, Enrico Trizio3, Hannah Bertschi4, Jacob Wright2, and Daniele Passerone1,2 — 1nanotech@surfaces laboratory, Empa, Zürich, Switzerland — 2ETH Zürich, Zürich, Switzerland — 3Italian Institute of Technology, Genova, Italy — 4Max Planck Institute, Hamburg, Germany
The interaction of chiral molecules with chiral surfaces plays a fundamental role in enantioselective catalysis and molecular recognition processes. In this work, we present a novel machine learning-assisted framework for enhanced sampling of chiral molecule dynamics on chiral PdGa surfaces. Using high-dimensional descriptors of molecular-surface interactions and leveraging state-of-the-art neural network potentials, our approach significantly accelerates the exploration of configurational space while maintaining chemical accuracy. Detailed analysis reveals how chiral PdGa surfaces influence molecular adsorption, orientation, and reaction pathways, providing new insights into the enantioselective mechanisms. These findings demonstrate the potential of integrating machine learning techniques with surface science to address challenges in heterogeneous catalysis.
Keywords: molecular dynamics; metadynamics; chirality; density-functional theory