CO₂ Utilization Through its Reduction to Methanol: Design of Catalysts Using Quantum Mechanics and Machine Learning

Reducing levels of CO₂, a greenhouse gas, in the earth's atmosphere is crucial to addressing the problem of climate change. An effective strategy to achieve this without compromising the scale of industrial activity involves use of renewable energy and waste heat in conversion of CO₂ to useful products. In this perspective, we present quantum mechanical and machine learning approaches to tackle various aspects of thermocatalytic reduction of CO₂ to methanol, using H₂ as a reducing agent. Waste heat can be utilized effectively in the thermocatalytic process, and H₂ can be generated using solar energy in electrolytic, photocatalytic and photoelectrocatalytic processes. Methanol being a readily usable fuel in automobiles, this technology achieves (a) carbon recycling process, (b) use of renewable energy, and (c) portable storage of H₂ for applications in automobiles, alleviating the problem of rising CO₂ emissions and levels in atmosphere.