Machine Learning in Catalysis, From Proposal to Practicing

Enhancing Ethylene Polymerization of NNN-Cobalt(II) Precatalysts Adorned with a Fluoro-substituent

Unsymmetrical 2-(1-(2,4-dibenzhydryl-6-fluorophenylimino)ethyl)-6-(1-alkylphenyl-imino)ethyl)pyridine compounds (Ar = 2,6-Me₂C₆H₃ in L1; 2,6-Et₂C₆H₃ in L2; 2,6- ⁱ Pr₂C₆H₃ in L3; 2,4,6-Me₃C₆H₂ in L4; 2,6-Et₂-4-Me-C₆H₂ in L5) were prepared and characterized. The treatment of CoCl₂ with the compounds L1-L5 afforded the corresponding cobalt complexes Co1-Co5 in excellent yields. The molecular structures of Co3 and Co4 were determined by single-crystal X-ray diffraction, revealing the distorted-square-pyramidal geometry with three nitrogen atoms and two chlorine atoms around the cobalt center. Compared with previous bis(imino)pyridylcobalt analogues, all of the cobalt precatalysts displayed exceptionally higher activities toward ethylene polymerization with 1.32 × 10⁷ g (PE) mol^-1 (Co) h^-1 at 60 °C in the presence of a co-catalyst MAO or MMAO. These cobalt catalysts produced highly linear polyethylene (PE) waxes with vinyl end groups and low molecular weight (M _w up to 8.23 kg mol^-1) along with a relatively lower melting point (all-round T _ms < 128 °C). The narrow dispersity of resultant polyethylenes indicated the single-site active species of the catalytic system.

Applications of Zeolites to C1 Chemistry: Recent Advances, Challenges, and Opportunities

C1 chemistry, which is the catalytic transformation of C1 molecules including CO, CO₂ , CH₄ , CH₃ OH, and HCOOH, plays an important role in providing energy and chemical supplies while meeting environmental requirements. Zeolites are highly efficient solid catalysts used in the chemical industry. The design and development of zeolite-based mono-, bi-, and multifunctional catalysts has led to a booming application of zeolite-based catalysts to C1 chemistry. Combining the advantages of zeolites and metallic catalytic species has promoted the catalytic production of various hydrocarbons (e.g., methane, light olefins, aromatics, and liquid fuels) and oxygenates (e.g., methanol, dimethyl ether, formic acid, and higher alcohols) from C1 molecules. The key zeolite descriptors that influence catalytic performance, such as framework topologies, nanoconfinement effects, Brønsted acidities, secondary-pore systems, particle sizes, extraframework cations and atoms, hydrophobicity and hydrophilicity, and proximity between acid and metallic sites are discussed to provide a deep understanding of the significance of zeolites to C1 chemistry. An outlook regarding challenges and opportunities for the conversion of C1 resources using zeolite-based catalysts to meet emerging energy and environmental demands is also presented.

Electrocatalytic Hydrogenation of Biomass-Derived Organics: A Review

Sustainable energy generation calls for a shift away from centralized, high-temperature, energy-intensive processes to decentralized, low-temperature conversions that can be powered by electricity produced from renewable sources. Electrocatalytic conversion of biomass-derived feedstocks would allow carbon recycling of distributed, energy-poor resources in the absence of sinks and sources of high-grade heat. Selective, efficient electrocatalysts that operate at low temperatures are needed for electrocatalytic hydrogenation (ECH) to upgrade the feedstocks. For effective generation of energy-dense chemicals and fuels, two design criteria must be met: (i) a high H:C ratio via ECH to allow for high-quality fuels and blends and (ii) a lower O:C ratio in the target molecules via electrochemical decarboxylation/deoxygenation to improve the stability of fuels and chemicals. The goal of this review is to determine whether the following questions have been sufficiently answered in the open literature, and if not, what additional information is required:(1)What organic functionalities are accessible for electrocatalytic hydrogenation under a set of reaction conditions? How do substitutions and functionalities impact the activity and selectivity of ECH?(2)What material properties cause an electrocatalyst to be active for ECH? Can general trends in ECH be formulated based on the type of electrocatalyst?(3)What are the impacts of reaction conditions (electrolyte concentration, pH, operating potential) and reactor types?

Machine Learning Prediction of Surface Segregation Energies on Low Index Bimetallic Surfaces

Surface chemical composition of bimetallic catalysts can differ from the bulk composition because of the segregation of the alloy components. Thus, it is very useful to know how the different components are arranged on the surface of catalysts to gain a fundamental understanding of the catalysis occurring on bimetallic surfaces. First-principles density functional theory (DFT) calculations can provide deeper insight into the surface segregation behavior and help understand the surface composition on bimetallic surfaces. However, the DFT calculations are computationally demanding and require large computing platforms. In this regard, statistical/machine learning methods provide a quick and alternative approach to study materials properties. Here, we trained previously reported surface segregation energies on low index surfaces of bimetallic catalysts using various linear and non-linear statistical methods to find a correlation between surface segregation energies and elemental properties. The results revealed that the surface segregation energies on low index bimetallic surfaces can be predicted using fundamental elemental properties.

Prediction of catalytic activities of bis(imino)pyridine metal complexes by machine learning

This work demonstrates the potential of machine learning (ML) method to predict catalytic activity of transition metal complex precatalyst toward ethylene polymerization. For this purpose, 294 complexes and 15 molecular descriptors were selected to build the artificial neural network (ANN) model. The catalytic activity can be well predicted by the obtained ANN model, which was further validated by external complexes. Boruta algorithm was employed to explicitly decipher the importance of descriptors, illustrating the conjugated bond structure, and bulky substitutions are favorable for catalytic activity. The present work indicates that ML could give useful guidance for the new design of homogenous polyolefin catalyst.

Advances in dynamically controlled catalytic reaction engineering

Dynamically forced input oscillations exhibit ability to surpass classical thermodynamic barriers through reactor operation and surface resonance.