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| Format: | Recurso digital |
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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.19488476 |
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Table of Contents:
- <p>Initial release accompanying the manuscript:</p> <p>"Quantitative Inverse Design of Altermagnetic Materials via Interpretable Machine Learning"</p> <p>Contents:</p> <ul> <li>Trained XGBoost model (final_model_all.json)</li> <li>Descriptor computation notebook (descriptor.ipynb)</li> <li>Bayesian optimization pipeline (BO.py)</li> <li>Crystal structure dataset (POSCARS.zip, 3,851 structures in VASP POSCAR format)</li> </ul> <p>Requirements: Python ≥ 3.8, numpy, pandas, pymatgen, scipy, scikit-learn, xgboost ≥ 2.0, optuna</p>