PyRank
aiqm

torchani

TorchANI 2.0 is an open-source library that supports training, development, and research of ANI-style neural network interatomic potentials. It was originally developed and is currently maintained by the Roitberg group.
PyPI GitHub
More links
Download Stats
Verified repo
Monthly downloads
6.0K
Weekly downloads
1.7K
GitHub stars
563

Downloads in the 30 and 7 days to October 10, 2026. Refreshed weekly.

Package Information

Latest Version
2.9.0
Requires Python
>=3.10
Licence
MIT
Last Release
1 month ago
First Release
September 26, 2018
Total Releases
35
No reported malware or high/critical advisories · Details

The current version has no OSV.dev malware reports and no critical or high-severity vulnerabilities.

Package Insights

What's this?
Score breakdown - each component scores 0-100, weighted by percentage
Maintenance
80
Popularity
59
Adoption
77
Community
65
Provenance
100
How the maintenance status was determined
Last commit
40 days ago
Monthly downloads
5,985
Active
Activity within 30 days
• Stable
Activity within 31-180 days
Minimal
Activity within 181-365 days, or no activity data
At Risk
No activity for > 365 days, but > 10,000 monthly downloads
Archived
Repository flagged as archived on GitHub
How the popularity category was determined
Monthly downloads
5,985
Stars
563
Stars per 1k downloads
94.07
Industry Standard
> 10,000,000 monthly downloads
Hidden Gem
< 0.5 stars per 1k downloads and > 100,000 monthly downloads
• Community Favorite
> 5 stars per 1k downloads
Rising Star
Balanced ratio of stars to downloads
Unknown
Fewer than 1,000 monthly downloads, or no star data
How download momentum is calculated
Week daily avg
244
Month daily avg
200
Momentum
+22.2%
((week_daily_avg - month_daily_avg) / month_daily_avg) * 100
More insights and metrics
Python version support tiers
requires_python
>=3.10
Cutting Edge
Minimum supported version is 3.14 or newer
Modern
Minimum supported version is 3.11 to 3.13
• Legacy
Supports 3.10 or older (or Python 2)
Unknown
No requires_python specified or unparseable
License categories
License
MIT
• Permissive
MIT, Apache, BSD, ISC, Unlicense, CC0, etc.
Copyleft
GPL, LGPL, AGPL, MPL, EPL, etc.
Proprietary
Commercial, all rights reserved, custom EULAs
Unknown
No license specified, or none of the above match
How the release cadence was determined
Last release
42 days ago
Total releases
35
Releases/year
4.3
Rapid
Last release within 365 days and > 12 releases/year
• Regular
Last release within 365 days
Slow
Last release 366-730 days ago
Stalled
Last release > 730 days ago, or no releases
Unknown
No usable release date available
How contributor risk was determined
Contributors
20
Monthly downloads
5,985
High Load
≤ 2 contributors and ≥ 50,000 monthly downloads
Moderate Load
< 10 contributors and ≥ 500,000 monthly downloads
Low Load
< 25 contributors and ≥ 5,000,000 monthly downloads, or sole maintainer with ≥ 10,000 monthly downloads
• Balanced
Enough contributors for the package's popularity
Unknown
No contributor data available
Engagement
63
 
Issue Burden
5.3
per 1k stars
Discussions
0.0
per 1k stars
Fork Ratio
14.4%
contrib / forks
Stale Release
No
 
PR Velocity
Quiet
 
Not all downloads include detailed metadata. Breakdowns reflect the subset that does.

Downloads by OS

Linux 199
macOS 2

Python Versions

3.12
41.7% 83
3.10
29.6% 59
3.11
12.6% 25
3.13
10.6% 21
3.14
5.5% 11

Top Countries

1. US US
208
2. SG SG
16
3. DE DE
5
4. IN IN
4
5. CH CH
2
6. CN CN
2
7. JP JP
2
8. BR BR
1
9. FR FR
1
10. GB GB
1

Weekly Downloads

GitHub Stars

Dependencies

13

Used By

4

Repository details

Forks
139
Issues
3
Last Commit
1 month ago
Contributors
20
Open PRs
5
Archived
No

Data and sources

Tracking Since
September 26, 2026
Data Updated
October 11, 2026
Data Freshness
Fresh
Keywords
machine-learning neural-networks deep-learning chemistry torch energy
Sources and methodology