ALIGNN & ALIGNN-FF

The Atomistic Line Graph Neural Network (ALIGNN) introduces a graph convolution layer that explicitly models both two- and three-body interactions in atomistic systems. It composes two edge-gated graph convolution layers: one applied to the atomistic line graph L(g) (triplet interactions) and one to the atomistic bond graph g (pair interactions).

ALIGNN-FF is a universal force-field built on ALIGNN. It was trained on the JARVIS-DFT dataset (~75,000 materials and 4M+ energy/force entries) and supports any combination of 89 elements. Pretrained models can be fine-tuned or trained from scratch on new data.

Highlights

• Property prediction (regression & binary classification)
• Multi-output regression (e.g. energy + bandgap + DOS)
• Universal force-field (ALIGNN-FF) with ASE calculator
• Pretrained models on JARVIS, Materials Project, QM9, MOF datasets
• Multi-GPU training via torchrun (DistributedDataParallel)
• CLI entry points: train_alignn.py, pretrained.py, run_alignn_ff.py

Quick links

• Install ALIGNN
• Train your first model
• Use a pretrained model
• Run the ALIGNN-FF ASE calculator
• Performance benchmarks

Citing

If you use ALIGNN, please cite the relevant papers listed on the References page. The primary references are:

1. Choudhary, K., DeCost, B. Atomistic Line Graph Neural Network for improved materials property predictions. npj Comput Mater 7, 185 (2021).
2. Choudhary, K., et al. Unified graph neural network force-field for the periodic table. Digital Discovery (2023).

Correspondence

Please open issues at https://github.com/atomgptlab/alignn/issues or email drkamal@jhu.edu.