Performance of Metal-Catalyzed Hydrodebromination of Dibromomethane Analyzed by Descriptors Derived from Statistical Learning
Abstract
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.
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When most of the data variance can be explained by the first few principal components, they can act as a useful reduced feature set.
“When most of the data variance can be explained by the first few PCs, they can act as a useful reduced feature set (54,66,67).”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [67]
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- Authors
- Saadun, A. J., Pablo-García, S., Paunović, V., Li, Q., Sabadell-Rendón, A., Kleemann, K., Krumeich, F., López, N., & Pérez-Ramírez, J.
- Affiliations
- ETH Zurich (CH)
- Institute of Chemical Research of Catalonia, ICIQ, The Barcelona Institute of Science and Technology, Av. Països Catalans 16, 43007 Tarragona, Spain
- Institut Català d'Investigació Química (ES)
- Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, ETH Zurich, Vladimir-Prelog-Weg 1, 8093 Zurich, Switzerland
- Corresponding author
- López, N.
- Venue
- ACS Catalysis
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-04-10
- Volume
- 10
- Issue
- 11
- Pages
- 6129-6143
- ISSN
- 2155-5435 (ISSN-L)
- OpenAlex
- W3016108113
- Cited by
- 30
- References
- 74
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 3 · 2023: 3 · 2022: 6 · 2021: 8 · 2020: 4
- FWCI
- 2.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- ACS Catalysis (publishedVersion)
- Funders
- Eidgen?ssische Technische Hochschule Z?rich (ETH-43 18-1)
- Ministerio de Ciencia, Innovaci?n y Universidades (RTI2018-101394-B100)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Catalytic Processes in Materials Science
- Physical Sciences › Chemical Engineering › Catalysis › Catalysts for Methane Reforming
- Physical Sciences › Chemical Engineering › Catalysis › Catalysis and Oxidation Reactions
- Keywords
- hydrodebromination, dibromomethane, methyl bromide, semihydrogenation, metal catalysts, catalyst selectivity, catalyst deactivation, adsorption energy, machine learning descriptors, random forest regression, Bayesian inference, principal component analysis, catalyst screening
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