ML in Catalysis
Project Summary: This page highlights key contributions to the application of machine learning in catalysis, including both fundamental and applied aspects. The focus is on kinetic modeling, catalyst performance prediction, and ML-assisted reaction system design.
Key Contributions:
- Lead author of a comprehensive review in the Chemical Engineering Journal (CEJ) outlining current and emerging ML approaches in catalysis
- Lead author of a Perspective article in New Journal of Chemistry on AI for photocatalytic hydrogen production
Publications:
- Recent developments in the use of machine learning in catalysis: A broad perspective with applications in kinetics – CEJ (2025)
- Artificial Intelligence-Driven Advances in Photocatalytic Hydrogen Production – NJC (2025)
Figure 1: Framework for integrating machine learning in catalytic system design.
Figure 2: Addressing data limitations by combining experimental and synthetic datasets.
Figure 3: Molecular view, bandgap, experimental conditions and machine learning in photocatalysis.
Image Credits: Figures adapted from Araujo et al., Chemical Engineering Journal (2025). https://doi.org/10.1016/j.cej.2025.160872 and Araujo and Farrusseng, New Journal of Chemistry (2025). https://doi.org/10.1039/D5NJ00505A