CederGroupHub / chgnet
Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov
Why this job: works in the areas we cover, but does not say which job it does
What it does
CHGNet highlights its ability to study electron interactions and charge distribution in atomistic modeling with near DFT accuracy. The charge inference is realized by regularizing the atom features with DFT magnetic moments, which carry rich information about both local ionic environments and charge distribution.
Get started
pip install chgnet- Maintenance
- Active
- Last commit 58 days ago
- Stars
- 414
- +1 this week
- Licence
- Custom
- Read before business use
- Downloads for
- None listed
- No release files
- Latest version
- v0.4.2
- Released 13 months ago
- Built with
- Python
- 74% of the code
Is it looked after?
- Contributors
- Not stated
- Last 90 days
- Issues
- 94% closed
- 5 still open
- Pull requests
- 100 merged
- 3 waiting
- Releases
- 19
- 0 in the last year
- No contributing guide
- No security policy
- Code of conduct (opens in new tab)
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More: languages, topics and project record
Languages
- Python 74%
- C 22%
- Cython 1%
- CSS 1%
Topics
- atomistic-simulations
- charge-distribution
- charge-transport
- computational-materials-science
- force-fields
- graph-neural-networks
- machine-learning
Project record
- JobOther AI tools
- StatusListed
- Commits452
- Forks104
- Watching6
- Stars in 28 days+2
- CreatedFeb 24, 2023
- First seen by usNov 7, 2026
- CheckedNov 8, 2026