CO(2) Utilization Through its Reduction to Methanol: Design of Catalysts Using Quantum Mechanics and Machine Learning

Reducing levels of CO(2), 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 CO(2) to useful products. In this perspective, we present quantum mechanical and machine learning approaches to tackle various aspects of thermocatalytic reduction of CO(2) to methanol, using H(2) as a reducing agent. Waste heat can be utilized effectively in the thermocatalytic process, and H(2) 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 H(2) for applications in automobiles, alleviating the problem of rising CO(2) emissions and levels in atmosphere.