SchNetPack - Deep Neural Networks for Atomistic Systems
Project description
SchNetPack aims to provide accessible atomistic neural networks that can be trained and applied out-of-the-box, while still being extensible to custom atomistic architectures
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
schnetpack-2.0.1.tar.gz
(164.3 kB
view details)
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
schnetpack-2.0.1-py3-none-any.whl
(226.1 kB
view details)
File details
Details for the file schnetpack-2.0.1.tar.gz.
File metadata
- Download URL: schnetpack-2.0.1.tar.gz
- Upload date:
- Size: 164.3 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.9.16
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
7255e270deb0479fe11dbea18d301a6f73482723cd619ac486b00cd80c4268be
|
|
| MD5 |
05cca43aaf65641a4c8eeaca7cc8d18a
|
|
| BLAKE2b-256 |
48422a8d90b56317fe12f0faa0975850b259a7bb5600aa936dfe9815319cd94e
|
File details
Details for the file schnetpack-2.0.1-py3-none-any.whl.
File metadata
- Download URL: schnetpack-2.0.1-py3-none-any.whl
- Upload date:
- Size: 226.1 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.9.16
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
332fd54754f38acec97896c6f23f2de8adc33007736e65a0dc43f9a5775a6a65
|
|
| MD5 |
9c4a8222cf3e1e9944bcc75ba7531aac
|
|
| BLAKE2b-256 |
2bf274f80adfd98a8c472bc9aebea6055702f53459ce853ab8ddf1cd81282424
|
