Citation & License#
Citation#
Since AimsPy is part of the DeepH ecosystem and drives FHI-aims calculations, we recommend citing the following papers:
1. DeepH-pack#
The complete package featuring the latest implementation, methodology, and workflow of DeepH:
@article{li2026deeph,
title={DeepH-pack: A general-purpose neural network package for deep-learning electronic structure calculations},
author={Li, Yang and Wang, Yanzhen and Zhao, Boheng and Gong, Xiaoxun and Wang, Yuxiang and Tang, Zechen and Wang, Zixu and Yuan, Zilong and Li, Jialin and Sun, Minghui and Chen, Zezhou and Tao, Honggeng and Wu, Baochun and Yu, Yuhang and Li, He and da Jornada, Felipe H. and Duan, Wenhui and Xu, Yong },
journal={arXiv preprint arXiv:2601.02938},
year={2026}
}
2. DeepH-aims#
The paper describing the DeepH–FHI-aims integration workflow (in publishing):
[Authors]. [Title]. [Journal], in publishing.
3. FHI-aims#
The original FHI-aims paper, since AimsPy drives FHI-aims calculations:
@article{blum2009ab,
title={Ab initio molecular simulations with numeric atom-centered orbitals},
author={Blum, Volker and Gehrke, Ralf and Hanke, Felix and Havu, Paula and Havu, Ville and Ren, Xinguo and Reuter, Karsten and Scheffler, Matthias},
journal={Comput. Phys. Communi.},
volume={180},
number={11},
pages={2175},
year={2009},
publisher={Elsevier}
}
License#
AimsPy is released under the GNU General Public License v3.0 or later (GPL-3.0-or-later).
Copyright (C) 2026 DeepH-pack developers
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
What does this mean?#
Freedom to use: You can use AimsPy for any purpose
Freedom to study: You have access to the source code
Freedom to modify: You can modify the code to suit your needs
Freedom to distribute: You can share your modifications, but they must also be under GPL-3.0-or-later
For more details, see the full GPL-3.0 license or the LICENSE file in the repository.
FHI-aims is not distributed with AimsPy#
AimsPy loads a patched libaims.so at runtime, but FHI-aims itself is not distributed with AimsPy and remains under its own licence agreement with the aims team. Users must obtain the FHI-aims source code independently from the aims team. The bundled patch is an open-source diff against a clean FHI-aims checkout; applying it does not change FHI-aims’ licensing for the underlying code.
Acknowledgements#
AimsPy is made possible by the collective efforts of many individuals and organizations:
Core Development Team#
The primary developers and maintainers who have contributed significant time and expertise to build and improve AimsPy.
Community Contributors#
We extend our sincere gratitude to all community members who have:
Submitted bug reports and feature requests
Contributed code improvements and new features
Helped improve documentation and examples
Shared their use cases and provided valuable feedback
Supporting Institutions#
AimsPy development has been supported by research grants and computing resources from various academic institutions and funding agencies.
Open Source Community#
AimsPy builds upon the work of many open-source projects in the scientific Python ecosystem. We gratefully acknowledge the developers of:
NumPy for numerical computing foundations
h5py / HDF5 for efficient data storage
mpi4py / MPI for high-performance parallel computing
Click for the command-line interface framework
Sphinx / sphinx-book-theme / MyST-NB for the documentation infrastructure
FHI-aims — the electronic structure code that AimsPy drives, developed by the aims team
And many other essential libraries
How to Acknowledge#
When presenting work that uses AimsPy, please:
Cite our paper (see Citation section above)
Acknowledge the DeepH-pack team in presentations and publications
Consider contributing back improvements to benefit the community
Getting Help and Contributing#
Support Channels#
Documentation: This documentation is your first resource
GitHub Issues: For bug reports and feature requests
Examples Directory: For practical implementation guides
Ways to Contribute#
We welcome contributions from everyone! Here’s how you can help:
Report bugs - Help us identify issues
Suggest features - Share your ideas for improvements
Improve documentation - Fix typos, add examples, clarify explanations
Submit code - Fix bugs or implement new features
Share examples - Contribute scripts showcasing your use cases
Help others - Answer questions in the community
Contribution Guidelines#
Before contributing, please read our notes For Developers which includes:
Code style and conventions
Testing requirements
Documentation standards
Pull request process
Final Notes#
AimsPy is an ongoing project that continues to evolve with contributions from the materials science and computational physics communities. Your feedback, contributions, and use cases help shape the future development of this toolkit.
Whether you’re using AimsPy for research, education, or industry applications, we hope it serves as a valuable tool in your computational materials science workflow.