Executive Summary : | In the 21st century, growing global energy consumption and technological advancements have led to significant environmental challenges. The extensive utilization of fossil fuels leads to an energy crisis and a significant amount of carbon dioxide (CO2) is emitted into the atmosphere, resulting in disruptions to global ecosystems and contributing to climate change. As renewable electricity becomes more affordable, the electrochemical CO2 reduction reaction (CO2RR) emerges as a promising method to convert captured CO2 into valuable products such as carbon monoxide, methanol, methane, formaldehyde, formic acid, ethanol, ethylene, etc. that are widely used in the fuel and chemicals industry, facilitating a sustainable carbon-neutral economy. Nevertheless, present electrocatalytic CO2 reduction encounters obstacles such as slow reaction kinetics, complex pathways and competing water reduction reactions. Overcoming these challenges necessitates the development of highly reactive, selective and cost-effective electrocatalysts. To achieve this, the proposal aims to design a suitable electrocatalyst for CO2 reduction with specific properties, including low energy barriers, high catalytic performance, excellent product selectivity and affordability. The primary focus of this project is to create a computational framework that efficiently designs and screens new catalysts for the CO2RR process by combining density functional theory (DFT) with modern artificial intelligence and machine learning (AI-ML) techniques. By adopting this interdisciplinary approach, the project has the potential to advance our understanding of the structure-property relationship and contribute to the development of predictive models for designing effective electrocatalysts. Special attention will be given to investigating essential descriptors, such as free energy changes (ΔG), adsorption energy, reaction barriers, electronic structure, electrocatalytic overpotential, charge transfer, etc. Moreover, by utilizing high-throughput calculations of essential descriptors like adsorption energies, d-band center, coordination number and feature engineering involving electronic, geometric and energetic characteristics, well-developed machine learning algorithms will forecast the catalytic activity, optimal composition, active sites and CO2RR pathways for range of desirable materials. This approach will yield valuable insights into the underlying reaction mechanisms, contributing to the progress of superior electrocatalytic materials. For designing electrocatalysts, two-dimensional (2D) carbon-based materials and MXenes ( based on Ti and V) will be explored due to their exceptional catalytic activity, thermal stability, large surface area and distinctive electronic properties. Our ultimate objective is to establish a comprehensive database that provides crucial information to experimentalists, assisting them in selecting appropriate materials for the fabrication of CO2RR catalysts. |