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

Abstract

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.