Skip to main content
archive
Search Submit Donate Log in
Press Enter to search · Advanced search

Condensed Matter > Materials Science

arXiv:2601.02938 (cond-mat)
[Submitted on 6 Jan 2026]

Title:DeepH-pack: A general-purpose neural network package for deep-learning electronic structure calculations

Authors:Yang Li, Yanzhen Wang, Boheng Zhao, Xiaoxun Gong, Yuxiang Wang, Zechen Tang, Zixu Wang, Zilong Yuan, Jialin Li, Minghui Sun, Zezhou Chen, Honggeng Tao, Baochun Wu, Yuhang Yu, He Li, Felipe H. da Jornada, Wenhui Duan, Yong Xu
View a PDF of the paper titled DeepH-pack: A general-purpose neural network package for deep-learning electronic structure calculations, by Yang Li and 17 other authors
View PDF HTML (experimental)
Abstract: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.
Comments: 19 pages, 7 figures, 1 table
Subjects: Materials Science (cond-mat.mtrl-sci); Chemical Physics (physics.chem-ph); Computational Physics (physics.comp-ph)
Cite as: arXiv:2601.02938 [cond-mat.mtrl-sci]
  (or arXiv:2601.02938v1 [cond-mat.mtrl-sci] for this version)
  https://doi.org/10.48550/arXiv.2601.02938
arXiv-issued DOI via DataCite

Submission history

From: Yong Xu [view email]
[v1] Tue, 6 Jan 2026 11:31:01 UTC (4,364 KB)
Full-text links:

Access Paper:

    View a PDF of the paper titled DeepH-pack: A general-purpose neural network package for deep-learning electronic structure calculations, by Yang Li and 17 other authors
  • View PDF
  • HTML (experimental)
  • TeX Source
view license

Current browse context:

cond-mat.mtrl-sci
< prev   |   next >
new | recent | 2026-01
Change to browse by:
cond-mat
physics
physics.chem-ph
physics.comp-ph

References & Citations

  • NASA ADS
  • Google Scholar
  • Semantic Scholar
Loading...

BibTeX formatted citation

Data provided by:

Bookmark

BibSonomy Reddit

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
IArxiv Recommender (What is IArxiv?)
  • Author
  • Venue
  • Institution
  • Topic

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
We gratefully acknowledge support from our major funders, member institutions, , and all contributors.
About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)
Major funding support from
Simons Foundation Simons Foundation International Schmidt Sciences