High Throughput COF Discovery to Capture CO₂ using ML
Under the direction of Dr. Vidya Avasare (Department of Chemistry), researchers are exploring the potential of covalent organic frameworks (COFs) as efficient materials for capturing atmospheric CO₂ and reducing carbon footprints.
COFs have emerged as promising candidates for carbon capture, with their inherent structural features playing a key role in determining their effectiveness. To better understand and enhance their performance, researchers are employing computational methods to analyse the molecular properties that contribute to CO₂ absorption. Additionally, machine learning techniques are being integrated to accelerate the design and optimisation of high-performance COFs through high-throughput screening.
This research aims to develop next-generation carbon capture solutions, leveraging computational and AI-driven insights to address global climate challenges.
