Band gap prediction typically involves theoretical and computational methods. The most common approaches include:
1. Density Functional Theory (DFT): This is a quantum mechanical method used to investigate the electronic structure of many-body systems. DFT allows for the calculation of the band structure and, consequently, the band gap.
2. Machine Learning (ML) Models: Recently, ML techniques have been employed to predict band gaps from material compositions and structures. These models are trained on large datasets of known materials and can make predictions for new materials with unknown properties.
3. Empirical and Semi-Empirical Methods: These methods use experimentally obtained data to derive band gap values. They are less computationally intensive but might not be as universally applicable as ab-initio methods.
