Predicting Many Crystal Properties via an Adaptive Transformer-based Framework
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866912384446103552 |
|---|---|
| author | Xu, Haosheng Qian, Dongheng Wang, Jing |
| author_facet | Xu, Haosheng Qian, Dongheng Wang, Jing |
| contents | Machine learning has revolutionized many fields, including materials science. However, predicting properties of crystalline materials using machine learning faces challenges in input encoding, output versatility, and interpretability. We introduce CrystalBERT, an adaptable transformer-based framework integrating space group, elemental, and unit cell information. This novel structure can seamlessly combine diverse features and accurately predict various physical properties, including topological properties, superconducting transition temperatures, dielectric constants, and more. CrystalBERT provides insightful interpretations of features influencing target properties. Our results indicate that space group and elemental information are crucial for predicting topological and superconducting properties, underscoring their intricate nature. By incorporating these features, we achieve 91\% accuracy in topological classification, surpassing prior studies and identifying previously misclassified materials. This research demonstrates that integrating diverse material information enhances the prediction of complex material properties, paving the way for more accurate and interpretable machine learning models in materials science. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_18944 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Predicting Many Crystal Properties via an Adaptive Transformer-based Framework Xu, Haosheng Qian, Dongheng Wang, Jing Materials Science Mesoscale and Nanoscale Physics Machine Learning Machine learning has revolutionized many fields, including materials science. However, predicting properties of crystalline materials using machine learning faces challenges in input encoding, output versatility, and interpretability. We introduce CrystalBERT, an adaptable transformer-based framework integrating space group, elemental, and unit cell information. This novel structure can seamlessly combine diverse features and accurately predict various physical properties, including topological properties, superconducting transition temperatures, dielectric constants, and more. CrystalBERT provides insightful interpretations of features influencing target properties. Our results indicate that space group and elemental information are crucial for predicting topological and superconducting properties, underscoring their intricate nature. By incorporating these features, we achieve 91\% accuracy in topological classification, surpassing prior studies and identifying previously misclassified materials. This research demonstrates that integrating diverse material information enhances the prediction of complex material properties, paving the way for more accurate and interpretable machine learning models in materials science. |
| title | Predicting Many Crystal Properties via an Adaptive Transformer-based Framework |
| topic | Materials Science Mesoscale and Nanoscale Physics Machine Learning |
| url | https://arxiv.org/abs/2405.18944 |