Atmospheric Water Harvesting with MOFs and COFs

As part of an industrial-scale water-harvesting project, I run extensive Grand Canonical Monte Carlo (GCMC) simulations to evaluate multi-cycle water adsorption isotherms and hydrolytic stability of candidate Metal-Organic Framework (MOF, e.g. CALF-20) and Covalent-Organic Framework (COF) structures. To move past the computational bottleneck of traditional force-field methods, I’m developing machine learning potentials (MLPs) that accelerate water-uptake prediction by orders of magnitude without sacrificing atomistic accuracy — analyzing the resulting structure–property relationships to inform water-capture efficiency and regeneration energy for real-world deployment.