Revisiting the Canonicalization for Fast and Accurate Crystal Tensor Property Prediction
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866908655017787392 |
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| author | Hua, Haowei Yang, Jingwen Lin, Wanyu Zhou, Pan |
| author_facet | Hua, Haowei Yang, Jingwen Lin, Wanyu Zhou, Pan |
| contents | Predicting the tensor properties of crystalline materials is a fundamental task in materials science. Unlike scalar property prediction, which requires invariance, tensor property prediction requires maintaining O(3) group tensor equivariance. Achieving such equivariance typically demands specialized architectural designs, which substantially increase computational cost. Canonicalization, a classical technique for geometry, has recently been explored for efficient learning with symmetry.In this work, we revisit the problem of crystal tensor property prediction through the lens of canonicalization. Specifically, we demonstrate how polar decomposition, a simple yet efficient algebraic method, can serve as a form of canonicalization and be leveraged to ensure equivariant tensor property prediction. Building upon this insight, we propose a general O(3)-equivariant framework for fast and accurate crystal tensor property prediction, referred to as GoeCTP. By utilizing canonicalization, GoeCTP achieves high efficiency without requiring the explicit incorporation of equivariance constraints into the network architecture.Experimental results indicate that GoeCTP achieves the high prediction accuracy and runs up to 13 times faster compared to existing state-of-the-art methods, underscoring its effectiveness and efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_02372 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Revisiting the Canonicalization for Fast and Accurate Crystal Tensor Property Prediction Hua, Haowei Yang, Jingwen Lin, Wanyu Zhou, Pan Computational Engineering, Finance, and Science Predicting the tensor properties of crystalline materials is a fundamental task in materials science. Unlike scalar property prediction, which requires invariance, tensor property prediction requires maintaining O(3) group tensor equivariance. Achieving such equivariance typically demands specialized architectural designs, which substantially increase computational cost. Canonicalization, a classical technique for geometry, has recently been explored for efficient learning with symmetry.In this work, we revisit the problem of crystal tensor property prediction through the lens of canonicalization. Specifically, we demonstrate how polar decomposition, a simple yet efficient algebraic method, can serve as a form of canonicalization and be leveraged to ensure equivariant tensor property prediction. Building upon this insight, we propose a general O(3)-equivariant framework for fast and accurate crystal tensor property prediction, referred to as GoeCTP. By utilizing canonicalization, GoeCTP achieves high efficiency without requiring the explicit incorporation of equivariance constraints into the network architecture.Experimental results indicate that GoeCTP achieves the high prediction accuracy and runs up to 13 times faster compared to existing state-of-the-art methods, underscoring its effectiveness and efficiency. |
| title | Revisiting the Canonicalization for Fast and Accurate Crystal Tensor Property Prediction |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2410.02372 |