HIENet
Model overview
Interatomic potential: Energy with gradient-based forces and stress.
CPS 0.707 combines discovery, geometry optimization, and phonons. Inspect individual task results below.
Licenses are as declared by the submitter. Training dataset terms are listed separately below.
Model Info
Specifications
- Architecture
- gnn
- Outputs
- Energy with gradient-based forces and stress EFSG
- Openness
- Open source, open data OSOD
Discovery protocol
- Training
- structure to energy, force, stress S2EFS
- Evaluation
- initial structure to relaxed energy with structure relaxation IS2RE-SR
Training Set
MPtrj: 1.58M structures from 146k materials · MIT
Run this model
Use the submitted environment with the upstream instructions.
From a repository checkout, run uv run models/run_kappa.py --list-models to check checkpoint requirements. If this model requires one, add --checkpoint /path/to/checkpoint below. Then execute the printed command
for a phonon smoke test in the model's isolated environment.
uv run models/run_kappa.py --model hienet --print-cmd --dry-runSelect an available task above to inspect this model's results.
Model Authors
description
HIENet is a hybrid invariant-equivariant graph neural network interatomic potential that combines E(3) invariant and O(3) equivariant message passing layers for materials discovery. The model uses physics-informed gradient-based predictions to ensure all outputs satisfy key physical constraints including force conservation and rotational symmetries, enabling accurate prediction of energy, forces, and stress for crystalline materials.