Leakage-Aware Bandgap Prediction on the JARVIS-DFT Dataset: A Phase-Wise Feature Analysis
This work addresses data leakage issues in materials science for researchers, providing a curated dataset and baseline metrics, but it is incremental as it focuses on analysis and control rather than novel prediction methods.
The study tackled the problem of data leakage in bandgap prediction by curating a leakage-controlled dataset of 2280 materials from JARVIS-DFT, and found that tree-based models achieved R2 values of 0.88 to 0.90, with dielectric tensor components as key predictors.
In this study, we perform a systematic analysis of the JARVIS-DFT bandgap dataset and identify and remove descriptors that may inadvertently encode band-structure information, such as effective masses. This process yields a curated, leakage-controlled subset of 2280 materials. Using this dataset, a three-phase modeling framework is implemented that incrementally incorporates basic physical descriptors, engineered features, and compositional attributes. The results show that tree-based models achieve R2 values of approximately 0.88 to 0.90 across all phases, indicating that expanding the descriptor space does not substantially improve predictive accuracy when leakage is controlled. SHAP analysis consistently identifies the dielectric tensor components as the dominant contributors. This work provides a curated dataset and baseline performance metrics for future leakage-aware bandgap prediction studies.