Performance of Metal-Catalyzed Hydrodebromination of Dibromomethane Analyzed by Descriptors Derived from Statistical Learning

The catalyzed semi hydrogenation of dibromomethane (CH₂Br₂) to methyl bromide (CH₃Br) is a key step in the bromine mediated upgrading of methane. This study presents a cutting edge strategy combining Density Functional Theory (DFT), catalytic tests complemented with extensive characterization of a wide range of metals (Fe, Co, Ni, Cu, Ru, Rh, Ag, Ir, and Pt), and statistical tools for a computer assisted investigation of this reaction. Steady state catalytic tests identified four classes of silica-supported catalysts comprising of (i) poorly active (< 8%) Fe, Co, Cu, and Ag, (ii) Rh and Ni that exhibit intermediate CH₃Br selectivity (< 60%), (iii) Ir and Pt which display great propensity to produce CH₄ (> 50%), whereas the (iv) highest selectivity to CH3Br (up to 96%) is obtained over Ru. In-depth characterization of representative catalysts in fresh and used forms was done by X ray diffraction, inductively coupled plasma optical emission spectroscopy, N2 sorption, temperature programmed reduction, Raman spectroscopy, electron microscopy, and X-ray photoelectron spectroscopy. The dimensionality reduction performed on the 272 DFT 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 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.