References

If ALIGNN or ALIGNN-FF contributed to your work, please cite the relevant papers below.

Core

1. Choudhary, K. & DeCost, B. Atomistic Line Graph Neural Network for improved materials property predictions. npj Computational Materials 7, 185 (2021). Link
2. Choudhary, K., DeCost, B., Major, L., Butler, K., Thiyagalingam, J., Tavazza, F. Unified graph neural network force-field for the periodic table. Digital Discovery (2023). Link

Applications

1. Prediction of the Electron Density of States for Crystalline Compounds with ALIGNN. Link
2. Recent advances and applications of deep learning methods in materials science. Link
3. Designing High-Tc Superconductors with BCS-inspired Screening, DFT, and Deep-learning. Link
4. A Deep-learning Model for Fast Prediction of Vacancy Formation in Diverse Materials. Link
5. Graph neural network predictions of MOF CO₂ adsorption properties. Link
6. Rapid Prediction of Phonon Structure and Properties using ALIGNN. Link
7. Large Scale Benchmark of Materials Design Methods. Link
8. Prediction of Magnetic Properties in van der Waals Magnets using GNNs. Link
9. CHIPS-FF: Benchmarking universal force-fields. Link

Full publication list

A complete list of ALIGNN-related publications is maintained at jarvis-tools publications.

Funding

• NIST-MGI
• NIST-CHIPS