Regensburg 2022 – scientific programme
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O: Fachverband Oberflächenphysik
O 41: Graphene: Growth, Substrate Interaction, Intercalation, and Doping
O 41.3: Talk
Wednesday, September 7, 2022, 11:00–11:15, S052
Determining the stability and catalytic formation of graphene on liquid Cu using machine-learning potentials — •Hao Gao1, Valentina Belova2, Maciej Jankowski2, Hendrik H. Heenen1, Gilles Renaud2, and Karsten Reuter1 — 1Fritz-Haber-Institut der MPG, Berlin, Germany — 2ESRF, Grenoble, France
The recently discovered rapid, high-quality synthesis of graphene (Gr) on liquid Cu catalysts is microscopically still poorly understood. This is due to the difficult characterization of the Cu liquid surface. Especially in atomistic simulations, the large length and time scales necessary to reliably emulate the temporal evolution of the liquid are a major challenge. Corresponding molecular dynamics simulations require large simulation cells and need to span well into the nanosecond regime -- an endeavor presently intractable via first-principles methods. In this work we use computationally efficient machine-learning potentials (MLPs) trained to density-functional theory (DFT) data in order to extrapolate the first-principles predictive power to the required scales. Detailed benchmarking confirms that our MLP captures the involved physics well, accurately reproducing the experimentally determined Gr adsorption height. We apply the MLP to further study the catalytic mechanism of Gr synthesis in order to rationalize distinct reaction kinetics found experimentally. Our work draws a path for the use of reliably trained MLPs as a multiscale modeling technique to explore previously unchartered computational problems. In that we provide new insight into the domain of liquid metal catalysts which generally lack atomic-scale understanding.