Artificial intelligence and machine learning: Recent studies on carbondioxide sequestration

¹ Department of Chemistry, Humanities and Sciences, Guru Nanak Institutions Technical Campus, Ibrahimpatnam, India
² Department of Chemistry, Faculty of Applied Sciences, University Institute of Engineering and Technology, Guru Nanak University, Ibrahimpatnam, India

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Published online: 19 June 2026

Abstract

The rapidly increasing concentration of greenhouse gas emissions, especially carbondioxide (CO₂), has been one of the greatest worldwide environmental issues today due to the burning of fossil fuels, the production of electricity and the fast industrialization, which have caused worldwide warming and changing the climate. A large number of methods, such as chemical absorption, recovery of waste heat, carbon storage and capture, and renewable energy sources have been developed in order to lower CO₂ emissions. However, the cost and effectiveness of these conventional methods are inadequate. With the advent and developments performed in the fields of machine learning (ML) and artificial intelligence (AI) new possibilities for CO₂ collection process prediction and optimization have been explored. Computational methods such as molecular dynamics simulations, density functional theory (DFT), and quantum mechanics/molecular mechanics (QM/MM) approaches using advanced tools have been extensively used thereby paving a way for the more detailed understanding on reactivity and adsorption behaviour of CO₂ molecules. Additionally, biological systems such as microalgae and plants showed significant potential for CO₂ sequestration. This paper emphases on the current advancements in CO₂ sequestration using AI and machine learning methods which have been used to generate sustainable solutions for the environmental changes.