New Insight into CO₂ Sequestration: A Blended Approach of Artificial Intelligence and Machine Learning Tools

Abstract

Recent advances in the fields of artificial intelligence and machine learning have paved a way in solving the unsolved problems embarking into a new dimension, especially, when there is increase in complexity of molecules. Reports have shown the necessity to employ these techniques to address the environmental problems. Herein we report the CO2 sequestration process by means of artificial intelligence (AI) and machine learning (ML) tools. The AI and ML approaches adopted enhance the accuracy of the results and at the same time give scope to explore new strategies in understanding the CO2 sequestration process. Herein we considered the reported active compounds observed in traditional medicinal plants like Oscimum, Azadiracta, Psidium and Ficus leaves and Curcuma and, their interactions with CO2. The crystal structures of the active compounds, collected from NCBI portal, are used for all the calculations. To understand the probable interactions of CO2 with active components AI tool IBMRXN was used and the properties of molecules are evaluated. ML techniques are employed using density functional theory method. Keeping in view the complexity of the molecules, optimization of the molecules is carried out at M062X/6-31G(d) level of theory. HOMO-LUMO energy gaps and binding energies are calculated at M062X/6-311+G(d,p)//M062X/6-31G(d) level of theory.