DeepH-pack: A general-purpose neural network package for deep-learning electronic structure calculations
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917187086712832 |
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| author | Li, Yang Wang, Yanzhen Zhao, Boheng Gong, Xiaoxun Wang, Yuxiang Tang, Zechen Wang, Zixu Yuan, Zilong Li, Jialin Sun, Minghui Chen, Zezhou Tao, Honggeng Wu, Baochun Yu, Yuhang Li, He da Jornada, Felipe H. Duan, Wenhui Xu, Yong |
| author_facet | Li, Yang Wang, Yanzhen Zhao, Boheng Gong, Xiaoxun Wang, Yuxiang Tang, Zechen Wang, Zixu Yuan, Zilong Li, Jialin Sun, Minghui Chen, Zezhou Tao, Honggeng Wu, Baochun Yu, Yuhang Li, He da Jornada, Felipe H. Duan, Wenhui Xu, Yong |
| contents | In computational physics and materials science, first-principles methods, particularly density functional theory, have become central tools for electronic structure prediction and materials design. Recently, rapid advances in artificial intelligence (AI) have begun to reshape the research landscape, giving rise to the emerging field of deep-learning electronic structure calculations. Despite numerous pioneering studies, the field remains in its early stages; existing software implementations are often fragmented, lacking unified frameworks and standardized interfaces required for broad community adoption. Here we present DeepH-pack, a comprehensive and unified software package that integrates first-principles calculations with deep learning. By incorporating fundamental physical principles into neural-network design, such as the nearsightedness principle and the equivariance principle, DeepH-pack achieves robust cross-scale and cross-material generalizability. This allows models trained on small-scale structures to generalize to large-scale and previously unseen materials. The toolkit preserves first-principles accuracy while accelerating electronic structure calculations by several orders of magnitude, establishing an efficient and intelligent computational paradigm for large-scale materials simulation, high-throughput materials database construction, and AI-driven materials discovery. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_02938 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DeepH-pack: A general-purpose neural network package for deep-learning electronic structure calculations Li, Yang Wang, Yanzhen Zhao, Boheng Gong, Xiaoxun Wang, Yuxiang Tang, Zechen Wang, Zixu Yuan, Zilong Li, Jialin Sun, Minghui Chen, Zezhou Tao, Honggeng Wu, Baochun Yu, Yuhang Li, He da Jornada, Felipe H. Duan, Wenhui Xu, Yong Materials Science Chemical Physics Computational Physics In computational physics and materials science, first-principles methods, particularly density functional theory, have become central tools for electronic structure prediction and materials design. Recently, rapid advances in artificial intelligence (AI) have begun to reshape the research landscape, giving rise to the emerging field of deep-learning electronic structure calculations. Despite numerous pioneering studies, the field remains in its early stages; existing software implementations are often fragmented, lacking unified frameworks and standardized interfaces required for broad community adoption. Here we present DeepH-pack, a comprehensive and unified software package that integrates first-principles calculations with deep learning. By incorporating fundamental physical principles into neural-network design, such as the nearsightedness principle and the equivariance principle, DeepH-pack achieves robust cross-scale and cross-material generalizability. This allows models trained on small-scale structures to generalize to large-scale and previously unseen materials. The toolkit preserves first-principles accuracy while accelerating electronic structure calculations by several orders of magnitude, establishing an efficient and intelligent computational paradigm for large-scale materials simulation, high-throughput materials database construction, and AI-driven materials discovery. |
| title | DeepH-pack: A general-purpose neural network package for deep-learning electronic structure calculations |
| topic | Materials Science Chemical Physics Computational Physics |
| url | https://arxiv.org/abs/2601.02938 |