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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...

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The theory of catalytically perfect enzymes was first proposed by W.J. Albery and J. R. Knowles in 1976. These enzymes catalyze biochemical reactions at high-speed. Their catalytic efficiency values range from 108-109 M-1s-1. These enzymes are also called 'diffusion-controlled' as the only rate-limiting step in the catalysis is that of the substrate diffusion into the active site. Examples include triose phosphate isomerase, fumarase, and superoxide dismutase.

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Updated: Jun 21, 2026

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

Fine-tuning large language models to generate single-atom catalyst synthesis procedures.

Manu Suvarna^1,2, Matteo Manica³, Fillipo Ficarra³

¹Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland. suvarnam@uni-greifswald.de.

Communications Chemistry

|June 19, 2026

Summary

This summary is machine-generated.

Fine-tuning large language models (LLMs) improves the generation of single-atom catalyst (SAC) synthesis protocols. These AI-assisted tools aid researchers in designing novel catalysts, driving innovation in catalysis.

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Area of Science:

Catalysis
Materials Science
Artificial Intelligence

Background:

Heterogeneous catalyst design is complex and knowledge-intensive.
General large language models (LLMs) struggle with specialized systems like single-atom catalysts (SACs).
Rapid growth in catalysis literature necessitates advanced knowledge management.

Purpose of the Study:

To fine-tune LLMs for generating chemically relevant SAC synthesis procedures.
To develop an AI-assisted tool for catalysis research.
To improve the efficiency and accessibility of catalyst design.

Main Methods:

Curated a dataset of 2,964 SAC publications.
Trained a Granite-based LLM on the SAC dataset.
Developed a user interface for querying synthesis protocols.
Conducted human-in-the-loop validation of generated protocols.

Main Results:

Fine-tuned LLM successfully generated coherent and context-aware SAC synthesis procedures.
Model recommendations aligned with experimental logic.
User interface provided tailored protocols based on specific design conditions.
Validation identified both strengths and limitations of the LLM-generated protocols.

Conclusions:

Fine-tuned LLMs can significantly enhance the generation of SAC synthesis protocols.
LLM-generated protocols serve as valuable assistive tools for researchers.
AI-assisted synthesis planning has the potential to accelerate innovation in catalysis.
Critical review of LLM outputs remains essential for reliable application.