Understanding molecules. Designing materials. Bridging simulation and experiment.
At MODEL, this is the question we pursue:
“Why does a material perform the way it does, how reliably can we predict it, and how can we design it better?”
Our research integrates molecular simulation, high-throughput screening, machine learning, materials characterization, and experimental gas adsorption measurements to understand the structure–property relationships that govern the performance of porous materials.
Our work spans four interconnected research directions:
Porous Materials for Energy and Molecular Separations
We design and evaluate porous materials for carbon dioxide capture, natural gas purification, hydrogen-related separations, gas storage, and membrane-based molecular separations.
Flexible and Responsive MOFs
We investigate how structural flexibility, adsorption-induced transformations, pressure, temperature, and guest molecules control the behaviour of dynamic porous materials. Rather than treating porous materials as static structures, we aim to understand how structure, dynamics, chemistry, environment, and processing collectively determine material performance.
Biomedical and Sustainable Porous Materials
We explore biocompatible MOFs for drug delivery and uremic toxin separation, with increasing emphasis on sustainable synthesis, life-cycle assessment, and environmentally responsible materials design.
Data-Driven Molecular Discovery
We integrate molecular simulations with high-throughput screening, machine learning, and emerging AI approaches to navigate large chemical spaces and identify structure–property relationships that cannot be captured by individual simulations alone.
Metal–organic frameworks (MOFs) received worldwide recognition with the 2025 Nobel Prize in Chemistry, awarded to Susumu Kitagawa, Richard Robson, and Omar M. Yaghi for the development of MOFs.