A composition-based ML framework for predicting electronic band gaps of inorganic materials. XGBoost & Random Forest on 15,537 Materials Project compounds using Magpie descriptors.
python data-science machine-learning dft random-forest scikit-learn regression open-science density-functional-theory computational-chemistry xgboost feature-engineering materials-science materials-informatics semiconductors pymatgen matminer band-gap materials-project compositional-descriptors
-
Updated
Aug 26, 2026 - Jupyter Notebook