Band Gap

Band gap, the energy difference between a material's valence and conduction bands, is crucial for determining its electronic and optical properties, impacting applications in electronics and optoelectronics. Current research focuses on accurately predicting band gaps using machine learning, employing techniques like message-passing neural networks and random forests, often incorporating readily available material properties to bypass computationally expensive methods like density functional theory. These advancements enable faster screening of materials for desired properties, accelerating the discovery and development of new functional materials with tailored band gaps for specific technological applications.