Official Resources
- Source Repository: https://github.com/ICAMS/grace-tensorpotential
- License: Open source
Overview
GRACE (Graph Atomic Cluster Expansion) is a foundation model for interatomic potentials combining ACE features with graph neural network architecture. The GRACE-2L model (15.3M parameters) achieves state-of-art accuracy on OMat24 and MPTraj datasets.
Scientific domain: Graph ACE foundation model for universal potentials
Target user community: Researchers needing high-accuracy universal potential with ACE features
Theoretical Methods
- Graph Atomic Cluster Expansion
- ACE polynomial basis + GNN
- Foundation model approach
- 15.3M parameters (2L model)
- Active learning support
Capabilities (CRITICAL)
- Foundation model (89+ elements)
- ACE + GNN architecture
- 15.3M parameters (2L)
- OMat24/MPTraj training
- Active learning integration
Sources: GitHub repository, npj Comput. Mater. 11, 50 (2025)
Key Strengths
ACE + GNN:
- Systematic ACE completeness
- GNN flexibility
- Best of both worlds
- Physically motivated
Foundation:
- 89+ elements
- Pretrained model
- Fine-tuning support
- Active learning
Inputs & Outputs
- Input formats: Structures (ASE)
- Output data types: Energies, forces, stresses
Interfaces & Ecosystem
- ASE: Calculator
- LAMMPS: MD engine
- Python: Core
Performance Characteristics
- Speed: Fast (GPU)
- Accuracy: State-of-art
- System size: 1-10000+ atoms
Computational Cost
- MD: ~1000x faster than DFT
- Fine-tuning: Hours
Limitations & Known Constraints
- New project: Still maturing
- GPU required: For production
- ICAMS maintained: Small team
Comparison with Other Codes
- vs MACE: GRACE has ACE basis, MACE is pure equivariant
- vs ACE1pack: GRACE is GNN+ACE, ACE1pack is pure ACE
- Unique strength: Graph ACE foundation model combining systematic ACE completeness with GNN flexibility
Application Areas
Universal MD:
- High-accuracy MD
- Phase stability
- Mechanical properties
- Active learning workflows
Best Practices
- Use GRACE-2L for best accuracy
- Fine-tune for specific chemistry
- Validate with DFT benchmarks
Community and Support
- Open source
- ICAMS maintained
- Published in npj Computational Materials
Verification & Sources
Primary sources:
- GitHub: https://github.com/ICAMS/grace-tensorpotential
Confidence: VERIFIED
Verification status: ✅ VERIFIED
- Source code: ACCESSIBLE (GitHub)
- Specialized strength: Graph ACE foundation model combining systematic ACE completeness with GNN flexibility