Data mining for materials science
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Aug 31, 2026 - HTML
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matminer
Here are 6 public repositories matching this topic...
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
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Aug 26, 2026 - Jupyter Notebook
Data-variance capture ability of Composition-descriptors while predicting the band gap (primarily semiconductor family choosen)
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Apr 13, 2026 - Python
Machine learning model to predict band gap of iron-oxide materials using Materials Project database and matminer chemistry features. Built with Python, scikit-learn and Random Forest achieving R² of 0.665.
python machine-learning random-forest materials-science materials-informatics matminer band-gap band-gap-prediction
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Aug 7, 2026 - Python
Inverse catalyst design with GP surrogates and multi-objective BO. Validated on published propane-dehydrogenation data: recovers Ga-Mo top-yield and Mg-modified low-deactivation families.
materials-informatics gradio gaussian-processes bayesian-optimization gpytorch botorch matminer inverse-design catalyst-design propane-dehydrogenation
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Aug 8, 2026 - Python
Discovery of new high entropy alloy using materials informatics and machine learning
python machine-learning genetic-algorithm materials-informatics metallurgy matminer inverse-design high-entropy-alloys chgnet material-science-engineering
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Aug 29, 2026 - Python
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