glgkghh7539/Altermagnet-inverse-design: v1.0 — Initial release
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| Natura: | Recurso digital |
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Zenodo
2026
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| _version_ | 1866902035037683712 |
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| author | glgkghh7539 |
| author_facet | glgkghh7539 |
| 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> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19488476 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | glgkghh7539/Altermagnet-inverse-design: v1.0 — Initial release glgkghh7539 <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> |
| title | glgkghh7539/Altermagnet-inverse-design: v1.0 — Initial release |
| url | https://doi.org/10.5281/zenodo.19488476 |