alignn 2026.4.2

alignn

📖 Full documentation: https://atomgptlab.github.io/alignn/

Table of Contents

• Introduction
• Installation
• Examples
• Pre-trained models
• JARVIS-ALIGNN webapp
• ALIGNN-FF & ASE Calculator
• Peformances on a few datasets
• Useful notes
• References
• How to contribute
• Correspondence
• Funding support

ALIGNN & ALIGNN-FF (Introduction)

The Atomistic Line Graph Neural Network (paper) introduces a graph convolution layer that explicitly models both two- and three-body interactions in atomistic systems. The ALIGNN-FF variant (paper) extends this to a force-field for structurally and chemically diverse systems across 89 elements.

See docs/index.md for the full introduction.

Installation

See docs/installation.md for conda, GitHub, and pip installation methods.

Examples

See docs/training/ for dataset format and training examples:

• Dataset format
• Single-output regression
• Classification
• Multi-output regression
• Force-field training
• Multi-GPU training

Google Colab notebooks are linked from docs/index.md.

Using pre-trained models

See docs/pretrained/:

• Property predictor
• ALIGNN-FF

Web-apps

See docs/usage/webapps.md. Direct links: AtomGPT ALIGNN app, ALIGNN-FF app.

ALIGNN-FF ASE Calculator

See docs/usage/ase-calculator.md for example usage.

Performances

See docs/performance.md for benchmark tables on JARVIS-DFT, Materials Project, QM9, hMOF, qMOF, OpenCatalyst, and other datasets. Also see JARVIS-Leaderboard.

Useful notes

See docs/notes.md for common pitfalls and FAQs.

References

See docs/references.md for the publication list.

How to contribute

See Contribution instructions and docs/contributing.md.

Correspondence

Please report bugs as GitHub issues or email [e-mail].

Funding support

• NIST-MGI
• NIST-CHIPS

Code of conduct

Please see Code of conduct.