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Garrido Torres, José A.

Publications and source records attributed to Garrido Torres, José A..

Symmetry-resolved CO desorption and oxidation dynamics on O/Ru(0001) probed at the C K-edge by ultrafast X-ray spectroscopy

In this work, we report on carbon monoxide desorption and oxidation induced by 400 nm femtosecond laser excitation on the O/Ru(0001) surface probed by time-resolved X-ray absorption spectroscopy (TR-XAS) at the carbon K-edge. The experiments were performed under constant background pressures of CO (6x10 -8 Torr) and O 2 (3x10 -8 Torr). Under these conditions, we detect two transient CO species with narrow 2π* peaks, suggesting little 2π* interaction with the surface. Based on polarization measurements, we find that these two species have opposing orientations: (1) CO favoring a more perpendicular orientation and (2) CO favoring a more parallel orientation with respect to the surface. We also directly detect gas-phase CO 2 using a mass spectrometer and observe weak signatures of bent adsorbed CO 2 at slightly higher X-ray energies than the 2π* region. These results are compared to previously reported TR-XAS results at the O K-edge where the CO background pressure was three times lower (2x10 -8 Torr) while maintaining the same O 2 pressure. At the lower CO pressure, in the CO 2π* region, we observed adsorbed CO and a distribution of OC-O bond lengths close to the CO oxidation transition state, with little indication of gas-like CO. The shift towards 'gas-like' CO species may be explained by the higher CO exposure, which blocks O adsorption, decreasing O coverage and increasing CO coverage. These effects decrease the CO desorption barrier through dipole-dipole interaction, while simultaneously increasing the CO oxidation barrier.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Machine learning with bond information for local structure optimizations in surface science

Local optimization of adsorption systems inherently involves different scales: within the substrate, within the molecule, and between the molecule and the substrate. In this work, we show how the explicit modeling of different characteristics of the bonds in these systems improves the performance of machine learning methods for optimization. Furthermore, we introduce an anisotropic kernel in the Gaussian process regression framework that guides the search for the local minimum, and we show its overall good performance across different types of atomic systems. The method shows a speed-up of up to a factor of two compared with the fastest standard optimization methods on adsorption systems. Additionally, we show that a limited memory approach is not only beneficial in terms of overall computational resources but can also result in a further reduction of energy and force calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗