Deep-learning density functional perturbation theory
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arXiv
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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866910348210077696 |
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| author | Li, He Tang, Zechen Fu, Jingheng Dong, Wen-Han Zou, Nianlong Gong, Xiaoxun Duan, Wenhui Xu, Yong |
| author_facet | Li, He Tang, Zechen Fu, Jingheng Dong, Wen-Han Zou, Nianlong Gong, Xiaoxun Duan, Wenhui Xu, Yong |
| contents | Calculating perturbation response properties of materials from first principles provides a vital link between theory and experiment, but is bottlenecked by the high computational cost. Here a general framework is proposed to perform density functional perturbation theory (DFPT) calculations by neural networks, greatly improving the computational efficiency. Automatic differentiation is applied on neural networks, facilitating accurate computation of derivatives. High efficiency and good accuracy of the approach are demonstrated by studying electron-phonon coupling and related physical quantities. This work brings deep-learning density functional theory and DFPT into a unified framework, creating opportunities for developing ab initio artificial intelligence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_17892 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep-learning density functional perturbation theory Li, He Tang, Zechen Fu, Jingheng Dong, Wen-Han Zou, Nianlong Gong, Xiaoxun Duan, Wenhui Xu, Yong Computational Physics Materials Science Calculating perturbation response properties of materials from first principles provides a vital link between theory and experiment, but is bottlenecked by the high computational cost. Here a general framework is proposed to perform density functional perturbation theory (DFPT) calculations by neural networks, greatly improving the computational efficiency. Automatic differentiation is applied on neural networks, facilitating accurate computation of derivatives. High efficiency and good accuracy of the approach are demonstrated by studying electron-phonon coupling and related physical quantities. This work brings deep-learning density functional theory and DFPT into a unified framework, creating opportunities for developing ab initio artificial intelligence. |
| title | Deep-learning density functional perturbation theory |
| topic | Computational Physics Materials Science |
| url | https://arxiv.org/abs/2401.17892 |