DeepH-2: Enhancing deep-learning electronic structure via an equivariant local-coordinate transformer
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913215636570112 |
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| author | Wang, Yuxiang Li, He Tang, Zechen Tao, Honggeng Wang, Yanzhen Yuan, Zilong Chen, Zezhou Duan, Wenhui Xu, Yong |
| author_facet | Wang, Yuxiang Li, He Tang, Zechen Tao, Honggeng Wang, Yanzhen Yuan, Zilong Chen, Zezhou Duan, Wenhui Xu, Yong |
| contents | Deep-learning electronic structure calculations show great potential for revolutionizing the landscape of computational materials research. However, current neural-network architectures are not deemed suitable for widespread general-purpose application. Here we introduce a framework of equivariant local-coordinate transformer, designed to enhance the deep-learning density functional theory Hamiltonian referred to as DeepH-2. Unlike previous models such as DeepH and DeepH-E3, DeepH-2 seamlessly integrates the simplicity of local-coordinate transformations and the mathematical elegance of equivariant neural networks, effectively overcoming their respective disadvantages. Based on our comprehensive experiments, DeepH-2 demonstrates superiority over its predecessors in both efficiency and accuracy, showcasing state-of-the-art performance. This advancement opens up opportunities for exploring universal neural network models or even large materials models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_17015 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DeepH-2: Enhancing deep-learning electronic structure via an equivariant local-coordinate transformer Wang, Yuxiang Li, He Tang, Zechen Tao, Honggeng Wang, Yanzhen Yuan, Zilong Chen, Zezhou Duan, Wenhui Xu, Yong Computational Physics Materials Science Deep-learning electronic structure calculations show great potential for revolutionizing the landscape of computational materials research. However, current neural-network architectures are not deemed suitable for widespread general-purpose application. Here we introduce a framework of equivariant local-coordinate transformer, designed to enhance the deep-learning density functional theory Hamiltonian referred to as DeepH-2. Unlike previous models such as DeepH and DeepH-E3, DeepH-2 seamlessly integrates the simplicity of local-coordinate transformations and the mathematical elegance of equivariant neural networks, effectively overcoming their respective disadvantages. Based on our comprehensive experiments, DeepH-2 demonstrates superiority over its predecessors in both efficiency and accuracy, showcasing state-of-the-art performance. This advancement opens up opportunities for exploring universal neural network models or even large materials models. |
| title | DeepH-2: Enhancing deep-learning electronic structure via an equivariant local-coordinate transformer |
| topic | Computational Physics Materials Science |
| url | https://arxiv.org/abs/2401.17015 |