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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.

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...

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Updated: Jan 8, 2026

Synthesis of Metal Nanoparticles Supported on Carbon Nanotube with Doped Co and N Atoms and its Catalytic Applications in Hydrogen Production

Synthesis of Metal Nanoparticles Supported on Carbon Nanotube with Doped Co and N Atoms and its Catalytic Applications in Hydrogen Production

Published on: December 6, 2021

Diffusion Model-Guided Inverse Design of Bimetallic Catalysts for Ammonia Decomposition.

Jiaqi Yang¹, Kailong Ye², Shaohua Xie²

¹Department of Chemical Engineering, Worcester Polytechnic Institute, Worcester, Massachusetts 01609, United States.

Journal of the American Chemical Society

|December 19, 2025

Summary

Generative AI models, specifically diffusion models, accelerate the discovery of novel bimetallic alloy catalysts for efficient ammonia decomposition. This approach identifies cost-effective, high-performance catalysts for sustainable hydrogen production and emissions control.

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Last Updated: Jan 8, 2026

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Area of Science:

Materials Science
Catalysis
Artificial Intelligence

Background:

Generative AI and deep learning are revolutionizing materials design.
Identifying efficient catalysts in vast chemical spaces is a major challenge in catalysis.
Diffusion-based inverse design models offer a promising solution for materials screening.

Purpose of the Study:

To develop a machine learning-guided workflow for inverse design of bimetallic alloy catalysts.
To target low-carbon ammonia decomposition for emissions control and hydrogen production.
To leverage generative AI for efficient and cost-effective catalyst discovery.

Main Methods:

Employed a diffusion model for inverse design of bimetallic alloy catalysts.
Utilized nitrogen adsorption energy as a key descriptor for catalyst evaluation, inspired by multiscale modeling.
Decoupled generative and property-prediction components for enhanced flexibility and accuracy.

Main Results:

Identified low-cost, environmentally friendly bimetallic alloy catalysts.
Achieved excellent catalytic performance for ammonia decomposition.
Validated catalyst candidates theoretically and experimentally.

Conclusions:

The proposed machine learning workflow effectively designs high-performance catalysts.
This approach accelerates the discovery of materials for sustainable energy applications.
Decoupling generative and predictive models improves catalytic material design.