CNIS Lab

In the global endeavour to achieve carbon neutrality, the anthropogenic CO2 utilization is an integral element that has risen in importance over the past few decades for the production of commodity chemicals. The use of MXene materials towards CO2 capture and conversion has gained intense momentum unlocking several new directions toward CO2 catalytic usage. However, the vast MXene chemical compositions makes it highly difficult to screen over appropriate MXene materials without the guidance of surface chemical and catalytic descriptors. Thus, to discover key properties governing CO2 activation by MXenes using low-cost computational tools are an urgent need. To that end, an in silico machine learning (ML)-assisted strategies can help in identifying potential indicator for CO2 activation for the screening and optimizing catalyst. Herein, we employ density functional theory and ML methodology to find key descriptor by providing a viable pathway for the activation of CO2 over pure and defective MXenes. We also perform feature importance analysis to find key determining, and guiding descriptors for CO2 activation will pave the way for accelerating the search of MXene-based materials and catalysts for CO2 capture and use technologies.