atomistic-machine-learning / schnetpack
SchNetPack - Deep Neural Networks for Atomistic Systems
Why this job: says: neural networks; topics: neural-network, machine-learning
What it does
SchNetPack is a toolbox for the development and application of deep neural networks to the prediction of potential energy surfaces and other quantum-chemical properties of molecules and materials. It contains basic building blocks of atomistic neural networks, manages their training and provides simple access to common benchmark datasets. This allows for an easy implementation and evaluation of new models.
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Active
- Last commit 3 days ago
- Stars
- 943
- On GitHub
- Licence
- Custom
- Read before business use
- Downloads for
- None listed
- No release files
- Latest version
- v2.3.0
- Released 6 days ago
- Built with
- Python
- 98% of the code
Is it looked after?
- Contributors
- Not stated
- Last 90 days
- Issues
- 100% closed
- 1 still open
- Pull requests
- 453 merged
- 2 waiting
- Releases
- 12
- 2 in the last year
- No contributing guide
- No security policy
- No code of conduct
Similar repos
| Repo | Stars | Last commit | Licence | Downloads for |
|---|---|---|---|---|
| pytorch / pytorch | 105,450 | today | Custom | No downloads |
| PennyLaneAI / pennylane | 3,523 | today | Apache-2.0 | No downloads |
| ARM-software / armnn | 1,319 | 4 months ago | MIT | Linux |
More: languages, topics and project record
Languages
- Python 98%
- C++ 1%
- Shell <1%
Topics
- machine-learning
- neural-network
- quantum-chemistry
- condensed-matter
- molecular-dynamics
Project record
- JobAI libraries and models
- StatusListed
- Commits1,915
- Forks251
- Watching27
- Stars in 28 days+1
- CreatedSep 3, 2018
- First seen by usNov 7, 2026
- CheckedNov 10, 2026
