The PhD project focuses on the high-throughput screening of materials for carbon capture and utilization, leveraging molecular simulations and machine learning. By integrating these advanced techniques, we can rapidly identify and optimize materials that effectively capture CO2 and convert it into valuable products. This innovative approach accelerates the discovery process and enhances our capacity to develop sustainable solutions for climate change. We will create a library of effective carbon capture materials, including ionic liquids, metal-organic frameworks, and zeolites, using density functional theory (DFT) and molecular dynamics (MD) simulations. To enhance carbon utilization, we propose using CO2 as a mild oxidant in a non-oxidative alkane dehydrogenation process to produce olefins. This dual-purpose approach utilizes in-situ hydrogen for CO2 hydrogenation while simultaneously dehydrogenating alkanes to olefins. In silico high-throughput screening of bimetallic alloys will be conducted using an ab initio microkinetic modelling approach to identify promising catalyst candidates for CO2 reduction. Additionally, with the increasing availability of renewable energy, electrocatalytic CO2 reduction can be harnessed to selectively produce higher alcohols. To achieve this, catalysts that facilitate carbon-carbon coupling reactions are essential. We propose the rational design of transition metal surfaces capable of selectively converting CO2 into ethanol and propanol. By integrating these strategies, we envision a comprehensive framework for discovering new materials for CO2 capture and utilization, with significant potential for commercialization. Background required: Bachelors or Masters degree in Chemical Engineering, Mechanical Engineering, Materials Science, Chemistry, or Physics. Strong interest in conducting extensive molecular simulations using MD/DFT tools and machine learning techniques.
“Elevating Horizons Through Discovery and Ingenuity”