Project summary

Catalysis underpins modern society by enabling critical processes in agriculture, energy, and environmental protection. The discovery and rational design of advanced catalysts are therefore essential for accelerating scientific and technological progress. The integration of artificial intelligence (AI) is transforming catalyst research by enabling rapid materials discovery, property prediction, and the identification of structure–performance relationships. This project aims to develop a large language model (LLM)-driven framework for the high-throughput discovery of catalysts for the electrochemical carbon dioxide reduction reaction (CO₂RR).

Project objectives

By integrating scientific literature, computational and experimental databases, and density functional theory (DFT) datasets, the LLM will extract chemical knowledge, identify structure–activity relationships, generate and rank promising catalyst candidates, and recommend optimal compositions and surface structures for targeted computational validation.

Project tasks

By coupling the automated DFT workflows and machine learning models, the framework will accelerate catalyst screening by predicting activity, selectivity, stability, and reaction pathways, significantly reducing the time and computational cost required to identify efficient catalysts for the sustainable conversion of CO₂ into value-added fuels and chemicals.

• Conduct data collection from datasets and literature
• Fine-tune LLM models and perform molecular simulations
• Develop causal relationships among materials’ chemistry, synthesis and performance
• Interpret the results and validate the findings

Expected outcomes

Gaining hands-on experience in coding, data collection and analysis

Proposing catalyst candidates with improved performance

References

https://doi.org/10.1038/s41467-023-43118-0

https://doi.org/10.1038/s41524-026-02065-2

https://doi.org/10.1002/adma.202401288

Research environment and Supervisory team

This project will be conducted in the PartCat Laboratory at the UNSW School of Chemical Engineering, and will have access to the high-performance computing facilities. The project will be jointly supervised by Prof Rose Amal and Dr Jodie Yuwono. Students will work closely with members of the PartCat research group and collaborators with other universities.