Several challenges exist in band gap prediction for catalysis:
1. Complexity of Materials: Catalysts often involve complex compositions and structures, making accurate prediction difficult.
2. Computational Cost: High-accuracy methods like hybrid DFT or GW are computationally expensive, limiting their use for screening large material libraries.
3. Data Quality and Availability: ML models require large datasets of high-quality experimental or theoretical band gaps, which are not always available.
