The Governing Council of the University of Toronto

Purpose

The Project aims at the discovery and optimization of Metal-Organic Frameworks (MOFs) for carbon capture, utilization, and storage (CCUS) applications, particularly focusing on ocean carbon capture and electrochemical CO2 conversion. Leveraging the advanced capabilities of Large Language Models (LLMs) and AI-driven laboratory automation, the Project aims to rapidly identify and synthesize promising MOF candidates, a task traditionally hindered by the vast chemical diversity and slow experimental processes. The Project objective is to develop an LLM/AI driven framework towards MOF discovery, and then apply this framework towards applications in bicarbonate (ocean capture) and electrochemical CO2 reduction. Alongside these performance metrics, the Project will prioritize properties desirable for CCUS applications, such as moisture stability, synthesizability, low-toxicity, and alignment with industry standards. A significant outcome will be establishing a pioneering ML-guided framework for material discovery, contributing a novel dataset to the scientific community, thereby enhancing research and development in CCUS technologies.