Graph Neural Networks Based Deep Learning for Predicting Structural and Electronic Properties
Fuente:
arXiv
Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910751846825984 |
|---|---|
| author | Selvaraj, Selva Chandrasekaran |
| author_facet | Selvaraj, Selva Chandrasekaran |
| contents | This study presents a deep learning approach to predicting structural and electronic properties of materials using Graph Neural Networks (GNNs). Leveraging data from the Materials Project database, we construct graph representations of crystal structures and employ GNNs to predict multiple properties simultaneously. All crystal structures are from the Materials Project database, with a total of 158,874 structures used. Our model achieves high predictive accuracy across various properties, as indicated by \( R^2 \) values: 0.96 for density, 0.97 for formation energy, 0.54 for energy above hull, 0.47 for structural stability (is\_S), 0.76 for band gap, 0.86 for valence band maximum, 0.78 for conduction band minimum, and 0.82 for Fermi energy. These results demonstrate the potential of GNNs in materials science, offering a powerful tool for rapid screening and discovery of materials with desired properties. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_02331 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Graph Neural Networks Based Deep Learning for Predicting Structural and Electronic Properties Selvaraj, Selva Chandrasekaran Disordered Systems and Neural Networks Materials Science 74Axx, 74Bxx I.2; I.3; I.6 This study presents a deep learning approach to predicting structural and electronic properties of materials using Graph Neural Networks (GNNs). Leveraging data from the Materials Project database, we construct graph representations of crystal structures and employ GNNs to predict multiple properties simultaneously. All crystal structures are from the Materials Project database, with a total of 158,874 structures used. Our model achieves high predictive accuracy across various properties, as indicated by \( R^2 \) values: 0.96 for density, 0.97 for formation energy, 0.54 for energy above hull, 0.47 for structural stability (is\_S), 0.76 for band gap, 0.86 for valence band maximum, 0.78 for conduction band minimum, and 0.82 for Fermi energy. These results demonstrate the potential of GNNs in materials science, offering a powerful tool for rapid screening and discovery of materials with desired properties. |
| title | Graph Neural Networks Based Deep Learning for Predicting Structural and Electronic Properties |
| topic | Disordered Systems and Neural Networks Materials Science 74Axx, 74Bxx I.2; I.3; I.6 |
| url | https://arxiv.org/abs/2411.02331 |