What I Do

I utilise state-of-the-art computational methods, including quantum mechanics (DFT/Post-HF), machine learning (ML/AI) tools, and experimental techniques (from synthesis to analysis and testing), to design next-generation energy materials for catalysis and understand their atomic-scale properties. These methods enable the design and development of materials for catalytic applications, including NH₃-SCR, HER, and CO₂ chemistry, as well as Materials properties.

I work at the intersection of computational materials science, experimental chemistry, and artificial intelligence (AI), deploying techniques from quantum chemistry (e.g., DFT), solid-state physics, and machine learning (ML) to reveal atomic-level structure–property relationships. Using insights from large-scale calculations on HPC systems, I predict, design, and optimise materials for real-world technologies.

The research is led by Jamal Abdul Nasir.

Recent Highlights?

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See our Research.