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Heterogeneous Catalysis

Heterogeneous Catalysis

Heterogeneous catalysis involves a catalyst in a different phase from the reactants. It is a process where the catalyst and the reactants are in distinct phases, typically solid and gas or liquid.Most heterogeneous catalysts are metals, metal oxides, or acids. The list includes transition metals like iron (Fe), cobalt (Co), nickel (Ni), palladium (Pd), platinum (Pt), chromium (Cr), manganese (Mn), tungsten (W), silver (Ag), and copper (Cu). These metals possess partially vacant d orbitals that...

Reduction of Alkenes: Asymmetric Catalytic Hydrogenation

Reduction of Alkenes: Asymmetric Catalytic Hydrogenation

Catalytic hydrogenation of alkenes is a transition-metal catalyzed reduction of the double bond using molecular hydrogen to give alkanes. The mode of hydrogen addition follows syn stereochemistry.
The metal catalyst used can be either heterogeneous or homogeneous. When hydrogenation of an alkene generates a chiral center, a pair of enantiomeric products is expected to form. However, an enantiomeric excess of one of the products can be facilitated using an enantioselective reaction or an...

Catalysis

Catalysis

The presence of a catalyst affects the rate of a chemical reaction. A catalyst is a substance that can increase the reaction rate without being consumed during the process. A basic comprehension of a catalysts’ role during chemical reactions can be understood from the concept of reaction mechanisms and energy diagrams.

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Updated: Mar 16, 2026

Reverse Microemulsion-mediated Synthesis of Monometallic and Bimetallic Early Transition Metal Carbide and Nitride Nanoparticles

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Published on: November 27, 2015

High-Throughput Screening and Interpretable Machine Learning for Rational Design of Bimetallic Catalysts for Methane

Mingzhang Pan^1,2, Tian Zhang¹, Jiawei Dong¹

¹College of Mechanical Engineering, Guangxi University, Nanning, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|March 14, 2026

Summary

Designing efficient bimetallic catalysts for methane removal is crucial. This study integrates density functional theory (DFT) and machine learning to discover novel catalysts, accelerating sustainable development and improving natural gas aftertreatment systems.

Keywords:

bimetallic catalysts density functional theory high‐throughput screening machine learning particle swarm optimization

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Synthesis and Performance Characterizations of Transition Metal Single Atom Catalyst for Electrochemical CO2 Reduction

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Area of Science:

Catalysis
Materials Science
Computational Chemistry

Background:

Efficient methane removal is vital for sustainable development.
Bimetallic catalysts show promise for methane activation but face design challenges due to complex compositional spaces.
Developing rational design strategies for these catalysts is essential.

Purpose of the Study:

To introduce an integrated framework combining high-throughput density functional theory (DFT) and interpretable machine learning.
To accelerate the rational design of bimetallic catalysts for efficient methane removal.
To identify key descriptors governing methane activation and discover high-performance catalysts.

Main Methods:

Computational screening of face-centered-cubic (FCC) bimetallic catalyst surfaces using DFT.
Identification of key descriptors: bond cleavage energies (C─H) and methyl adsorption energy.
Training and selection of machine learning models using particle swarm optimization (PSO) and SHapley additive interpretability (SHAP) analysis.

Main Results:

Identified bond cleavage energies and methyl adsorption energy as key descriptors for successive C─H activation.
Developed interpretable machine learning models capable of accurately predicting C─H bond energies.
Discovered a bimetallic catalyst for consecutive C─H bond cleavages outperforming conventional aftertreatment systems.

Conclusions:

Established an interpretable, data-driven methodology for designing high-efficiency multicomponent catalysts.
Demonstrated the synergistic interaction of descriptors for effective machine learning model construction.
Accelerated catalyst design through the integration of high-throughput DFT and interpretable machine learning.