Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.06996 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911275732172800 |
|---|---|
| author | Ajaib, M. Adeel Nasir, Fariha Rehman, Abdul |
| author_facet | Ajaib, M. Adeel Nasir, Fariha Rehman, Abdul |
| contents | We explore machine learning techniques for predicting Curie temperatures of magnetic materials using the NEMAD database. By augmenting the dataset with composition-based and domain-aware descriptors, we evaluate the performance of several machine learning models. We find that the Extra Trees Regressor delivers the best performance reaching an R^2 score of up to 0.85 $\pm$ 0.01 (cross-validated) for a balanced dataset. We employ the k-means clustering algorithm to gain insights into the performance of chemically distinct material groups. Furthermore, we perform the SHAP analysis to identify key physicochemical drivers of Curie behavior, such as average atomic number and magnetic moment. By employing explainable AI techniques, this analysis offers insights into the model's predictive behavior, thereby advancing scientific interpretability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_06996 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Explainable AI for Curie Temperature Prediction in Magnetic Materials Ajaib, M. Adeel Nasir, Fariha Rehman, Abdul Materials Science Machine Learning We explore machine learning techniques for predicting Curie temperatures of magnetic materials using the NEMAD database. By augmenting the dataset with composition-based and domain-aware descriptors, we evaluate the performance of several machine learning models. We find that the Extra Trees Regressor delivers the best performance reaching an R^2 score of up to 0.85 $\pm$ 0.01 (cross-validated) for a balanced dataset. We employ the k-means clustering algorithm to gain insights into the performance of chemically distinct material groups. Furthermore, we perform the SHAP analysis to identify key physicochemical drivers of Curie behavior, such as average atomic number and magnetic moment. By employing explainable AI techniques, this analysis offers insights into the model's predictive behavior, thereby advancing scientific interpretability. |
| title | Explainable AI for Curie Temperature Prediction in Magnetic Materials |
| topic | Materials Science Machine Learning |
| url | https://arxiv.org/abs/2508.06996 |