Official Resources
- Source Repository: https://github.com/lab-cosmo/pet-mad
- License: Open source (MIT)
Overview
PET-MAD is a lightweight universal interatomic potential trained on r²SCAN data. Covering 94 elements (102 in v1.5), it achieves competitive accuracy with only 2.8M parameters, making it efficient for production MD.
Scientific domain: Lightweight universal potential on r²SCAN data
Target user community: Researchers needing efficient universal potential with meta-GGA accuracy
Theoretical Methods
- Point Edge Transformer (PET) architecture
- MAD dataset (r²SCAN-based)
- Lightweight model (2.8M parameters)
- 94-102 elements coverage
- Metatrain fine-tuning
Capabilities (CRITICAL)
- 94 elements (102 in v1.5)
- r²SCAN training data
- 2.8M parameters (lightweight)
- Metatrain fine-tuning
- ASE, i-PI, LAMMPS interfaces
Sources: GitHub repository, arXiv:2503.02089
Key Strengths
r²SCAN Training:
- Meta-GGA quality data
- Better than PBE-trained models
- Higher accuracy reference
- Chemical accuracy potential
Lightweight:
- 2.8M parameters
- Efficient inference
- Fast MD
- Low memory
Fine-Tuning:
- Metatrain framework
- Custom dataset adaptation
- Transfer learning
- Active learning
Inputs & Outputs
- Input formats: Structures (ASE)
- Output data types: Energies, forces, stresses
Interfaces & Ecosystem
- ASE: Calculator
- i-PI: MD interface
- LAMMPS: MD engine
- Metatrain: Fine-tuning
Performance Characteristics
- Speed: Fast (lightweight)
- Accuracy: r²SCAN-level
- System size: 1-10000+ atoms
Computational Cost
- MD: Very efficient
- Fine-tuning: Minutes to hours
Limitations & Known Constraints
- New project: Still maturing
- r²SCAN data: Limited dataset size
- Lightweight: May sacrifice some accuracy
Comparison with Other Codes
- vs MACE-MP-0: PET-MAD is r²SCAN, MACE is PBE
- vs CHGNet: PET-MAD is lighter, CHGNet has charge
- Unique strength: Lightweight universal potential (2.8M params) trained on r²SCAN meta-GGA data
Application Areas
Production MD:
- Efficient MD simulations
- Meta-GGA quality at PBE cost
- Structure relaxation
- Energy screening
Fine-Tuning:
- Custom chemistry
- Active learning
- Multi-fidelity
Best Practices
- Use for r²SCAN-quality MD
- Fine-tune with Metatrain
- Compare with PBE-trained models
Community and Support
- Open source (MIT)
- Lab Cosmo maintained
- Metatensor ecosystem
Verification & Sources
Primary sources:
- GitHub: https://github.com/lab-cosmo/pet-mad
Confidence: VERIFIED
Verification status: ✅ VERIFIED
- Source code: ACCESSIBLE (GitHub)
- Specialized strength: Lightweight universal potential (2.8M params) trained on r²SCAN meta-GGA data