The functionality of the materials used for energy applications is critically determined by the physical properties of small active regions, such as dopants, dislocations, interfaces, grain boundaries, etc. The capability to manipulate and utilize the inevitable disorder in materials, whether due to the finite-dimensional defects (such as vacancies, dopants, grain boundaries) or due to the complete atomic randomness (as in amorphous materials), can bring innovation in designing energy materials. With the increase in computational material science capabilities, it is now possible to understand the complexity present in materials due to various defects, resulting in pathways required for optimizing their efficiencies. In this talk, I will present a critical overview of recent computational advances in the design of sustainable materials for light- and electric field-driven CO₂ capture, as well as for CO₂ conversion into fuels and value-added chemicals through photocatalysis. I’ll provide a comprehensive review of our research efforts, which involve employing traditional methods like density functional theory (DFT), alongside leveraging the data they generate to implement machine learning techniques, thereby accelerating the discovery of transition metal-based materials for these vital applications.