Optimal Dynamic Regimes for CO Oxidation Discovered by Reinforcement Learning.

Metal nanoparticles are widely used as heterogeneous catalysts to activate adsorbed molecules and reduce the energy barrier of the reaction. Reaction product yield depends on the interplay between elementary processes: adsorption, activation, desorption, and reaction. These processes, in turn, depend on the inlet gas composition, temperature, and pressure.

Influence of strong π-acceptor ligands on Cr-K-edge X-ray absorption spectral signatures and consequences for the interpretation of surface sites in the Phillips catalyst.

X-ray absorption spectroscopy (XAS) has been central to the study of the Phillips polymerization catalyst (CrO/SiO). As Cr K-edge XAS signatures are sensitive to the oxidation state, geometry and types of ligands on surface (active) sites, the superposition of these effects makes their interpretation challenging. Notably, CO has been particularly used as a reductant to generate low valent Cr sites from CrO/SiO and as a structural IR probe for analysing reduced Cr surface sites.

Tracking Coordination Environment and Reaction Intermediates in Homogeneous and Heterogeneous Epoxidation Catalysts via Ti L-Edge Near-Edge X-ray Absorption Fine Structures.

Ti-based molecules and materials are ubiquitous and play a major role in both homogeneous and heterogeneous catalytic processes. Understanding the electronic structures of their active sites (oxidation state, local symmetry, and ligand environment) is key to developing molecular-level structure-property relationships. In that context, X-ray absorption spectroscopy (XAS) offers a unique combination of elemental selectivity and sensitivity to local symmetry.

Active Sites in Cr(III)-Based Ethylene Polymerization Catalysts from Machine-Learning-Supported XAS and EPR Spectroscopy.

The ethylene polymerization Phillips catalyst has been employed for decades and is central to the polymer industry. While Cr(III) alkyl species are proposed to be the propagating sites, there is so far no direct experimental evidence for such proposal. In this work, by coupling Surface organometallic chemistry, EPR spectroscopy, and machine learning-supported XAS studies, we have studied the electronic structure of well-defined silica-supported Cr(III) alkyls and identified the presence of several surface species in high and low-spin states, associated with different coordination environments.

Quantitative Structural Description of Zeolites by Machine Learning Analysis of Infrared Spectra.

Application of machine learning (ML) algorithms to spectroscopic data has a great potential for obtaining hidden correlations between structural information and spectral features. Here, we apply ML algorithms to theoretically simulated infrared (IR) spectra to establish the structure-spectrum correlations in zeolites. Two hundred thirty different types of zeolite frameworks were considered in the study whose theoretical IR spectra were used as the training ML set.

Adsorption Sites on Pd Nanoparticles Unraveled by Machine-Learning Potential with Adaptive Sampling.

Catalytic properties of noble-metal nanoparticles (NPs) are largely determined by their surface morphology. The latter is probed by surface-sensitive spectroscopic techniques in different spectra regions. A fast and precise computational approach enabling the prediction of surface-adsorbate interaction would help the reliable description and interpretation of experimental data.

Crystal Orbital Overlap Population and X-ray Absorption Spectroscopy.

We present an extension of an numerical tool calculating X-ray absorption spectroscopies and crystal orbital overlap populations at the same time. Density functional theory is used to calculate the electronic structure in both occupied (valence to core X-ray emission spectroscopy) and nonoccupied states (, X-ray absorption near the edge structure and X-ray magnetic circular dichroism) and to evaluate the orbital overlap typifying the covalency between neighboring atoms. We show how the different features in the experimental spectra can be correlated to the chemical bonds around the absorbing atoms in several examples including acrylonitrile molecules, rutile TiO, LiRuO high-energy density cathode, ZnO, and anti-ferromagnetic VO.

Simulation of Surface Resonant X-ray Diffraction.

We present an ab initio numerical tool to simulate surface resonant X-ray diffraction experiments. The crystal truncation rods and the spectra around a given X-ray absorption edge are calculated at any position of the reciprocal space. Density functional theory is used to determine the resonant scattering factor of an atom within its local environment and to calculate the diffraction peak intensities for surfaces covered with a thin film or with one or several adsorbed layers.

Full-Potential Simulation of X-ray Raman Scattering Spectroscopy.

We present a new ab initio way to calculate X-ray Raman scattering spectra within the independent electron approximation. Our approach avoids any approximation about the shape of the used potential and leads to good agreement between experiment and theory. We show that the momentum transfer dependence in two typical cases, the F K-edge in LiF and the B and N K-edges in hexagonal BN, is well-reproduced.