Benchmarking bandgap prediction in semiconductors under experimental and realistic evaluation settings

ArXi:2604.25568v1 Announce Type: cross Accurate bandgap prediction is crucial for semiconductor applications, yet machine learning models trained on computational data often struggle to generalize to experimental bandgap measurements. Challenges related to data fidelity, domain generalization, and model interpretability remain insufficiently addressed in existing evaluation frameworks. To bridge this gap, we