The catalyzed semihydrogenation of dibromomethane (CH 2 Br 2 ) to methyl bromide (CH 3 Br) is a key step in the bromine-mediated upgradation of methane. This study presents a cutting-edge strategy combining density functional theory (DFT), catalytic tests complemented with the extensive characterization of a wide range of metal catalysts (Fe, Co, Ni, Cu, Ru, Rh, Ag, Ir, and Pt), and statistical tools for a computer-assisted investigation of this reaction. The steady-state catalytic tests identified four classes of materials comprising (i) poorly active (<8%) Fe/SiO 2 , Co/SiO 2 , Cu/SiO 2 , and Ag/SiO 2 ; (ii) Rh/SiO 2 and Ni/SiO 2 , which exhibit intermediate CH 3 Br selectivity (<60%); (iii) Ir/SiO 2 and Pt/SiO 2 , which display great propensity to CH 4 (>50%); and (iv) Ru/SiO 2 , which exhibits the highest selectivity to CH 3 Br (up to 96%). In-depth characterization of representative catalysts in fresh and used forms was done by X-ray diffraction, inductively coupled plasma optical emission spectroscopy, N 2 sorption, temperature-programmed reduction, Raman spectroscopy, electron microscopy, and X-ray photoelectron spectroscopy. The dimensionality reduction performed on the 272 DFT intermediate adsorption energies using principal component analysis identified two descriptors that, when employed together with the experimental data in a random forest regressor, enabled the understanding of activity and selectivity trends by connecting them to the energy intervals of the descriptors. In addition, a representative analytic model was found using the Bayesian inference. These findings illustrate the exciting opportunities presented by integrated experimental/computational screening and set the fundamental basis for the accelerated discovery of superior hydrodebromination catalysts and beyond.

We report the promoting effect of graphitic carbon nitride in Cu-catalyzed N-arylation. The abundance of pyridinic coordination sites in this host permits the adsorption of copper iodide from the reaction medium. The key to achieving high activity is to confine active Cu species on the surface, which is accomplished by introducing atomically-dispersed metal dopants to block diffusion into the bulk. The alternative route of incorporating metal during the synthesis of graphitic carbon nitride is ineffective as Cu is thermodynamically more stable in inactive subsurface positions. A combination of X-ray absorption, X-ray photoelectron, and electron paramagnetic resonance spectroscopy, density functional theory, and Kinetic Monte Carlo simulations is employed to determine the location and associated geometry as well as the electronic structure of metal centers. N-arylation activity correlates to the surface coverage by copper, which varies during the reaction due to an interplay between site formation via adsorption from the reaction medium and deactivation by diffusion into the bulk of the material, and is highest when an Fe dopant is used that hinders movement through the lattice.

The mechanistic analysis in heterogeneous catalysis is based on listing all elementary steps and evaluating explicitly their energies. To this end, computational models based on Density Functional Theory have become a standard to estimate the information needed in mechanistic studies. Typically, either the minimum energy paths or those with the smaller span are summarized in reaction profiles. Such simplifications gather a lot of information, although further dimensionality reduction is required to obtain the most relevant descriptors of catalytic activity and generate the so‐called volcano plots. The selection of descriptors has been traditionally based on simple intermediates, such as central atoms in small molecules (as C in CH4), which have good thermodynamic correlations to other fragments containing them. Yet, in emerging processes (recent studies), the number of intermediates involved increase, configurational effects and lateral interactions become significant, and complex materials with low symmetry are employed, thus the simple rules encapsulated in linear scaling relationships lose their predictive power due to error accumulation. At the same time, large datasets generated for the intermediates call for statistical analysis and thus these techniques are being leveraged to chemical systems, particularly to reduce their dimensionality. This article is categorized under: Structure and Mechanism > Reaction Mechanisms and Catalysis Structure and Mechanism > Computational Materials Science Electronic Structure Theory > Ab Initio Electronic Structure Methods

Activity equations trying to mimic experimental catalytic performance derived from reaction profiles and microkinetic models have been the state of the art in modeling in the last decades. This approach has been able to reproduce semiquantitatively activity volcano plots leading to successful catalyst optimization through the use of descriptors. As systems become more complex (both catalysts and reactants), these methods face increasing limitations. Statistical Learning (SL) techniques can overcome these limitations and improve the search for descriptor-based performance equations. However, the black-box nature of SL techniques makes physical interpretation of the so-obtained models difficult. To advance in the integration of these methodologies to real problems, we have merged experimental activity and selectivity presented as a function of chemical descriptors from Density Functional Theory for the catalyzed hydrodehalogenation of CH2X2 (for X = Br, Cl) leading to three main products. The employed Bayesian procedure is able to identify robust equations for activity and selectivity as a function of only two descriptors. This work provides a starting point to solve complex reaction networks using a set of statistical learning tools and hybrid data.

Ceria-based single-atom catalysts present complex electronic structures due to the dynamic electron transfer between the metal atoms and the semiconductor oxide support. Understanding these materials implies retrieving all states in these electronic ensembles, which can be limiting if done via density functional theory. Here, we propose a data-driven approach to obtain a parsimonious model identifying the appearance of dynamic charge transfer for the single atoms (SAs). We first constructed a database of (701) electronic configurations for the group 9–11 metals on CeO2(100). Feature Selection based on predictive Elastic Net and Random Forest models highlights eight fundamental variables: atomic number, ionization potential, size, and metal coordination, metal–oxygen bond strengths, surface strain, and Coulomb interactions. With these variables a Bayesian algorithm yields an expression for the adsorption energies of SAs in ground and low-lying excited states. Our work paves the way towards understanding electronic structure complexity in metal/oxide interfaces.