Official Resources
- Homepage: https://amp.readthedocs.io/
- Documentation: https://amp.readthedocs.io/
- Source Repository: https://bitbucket.org/andrewpeterson/amp
- License: GPL-3.0
Overview
AMP (Atomistic Machine-learning Package) is an open-source package for fitting neural network potentials to atomistic data. It uses Behler-Parrinello symmetry functions as descriptors and provides a simple interface for training potentials from DFT data.
Scientific domain: Neural network potentials, atomistic machine learning
Target user community: Researchers training neural network potentials
Theoretical Methods
- Behler-Parrinello symmetry functions
- Neural network regression
- Gaussian descriptor functions
- Atomic fingerprints
Capabilities (CRITICAL)
- Neural network potential training
- Symmetry function descriptors
- ASE calculator interface
- Parallel training
- Multiple descriptor types
Key Strengths
Simplicity:
- Easy to use
- Good documentation
- ASE integration
Flexibility:
- Custom descriptors
- Multiple backends
Inputs & Outputs
- Input formats: ASE trajectory, VASP OUTCAR
- Output data types: Trained models, energies, forces
Interfaces & Ecosystem
- ASE: Calculator interface
- AmpTorch: PyTorch version
Advanced Features
- Symmetry functions: G2, G4 types
- Parallel training: MPI support
- Custom descriptors: Extensible
Performance Characteristics
- CPU-based training
- Good for small datasets
- ASE integration efficient
Computational Cost
- Training: Hours (CPU)
- Inference: Fast
- Overall: Moderate
Best Practices
- Use appropriate symmetry function parameters
- Validate on test set
- Consider AmpTorch for GPU acceleration
- Start with default descriptors
Limitations & Known Constraints
- CPU-based (AmpTorch for GPU)
- Older architecture (pre-equivariant era)
- Less accurate than equivariant methods
- Symmetry function choice critical
Application Areas
- Surface science
- Catalysis
- Materials modeling
- Educational purposes
Comparison with Other Codes
- vs AmpTorch: AMP CPU-based original, AmpTorch GPU PyTorch version
- vs NequIP/MACE: AMP descriptor-based, others equivariant (more accurate)
- vs N2P2: Both Behler-Parrinello style, different implementations
- Unique strength: Simplicity, good documentation, educational value, ASE integration
Community and Support
- Established codebase
- Good documentation
- ASE community
- Published tutorials
Verification & Sources
Primary sources:
- Documentation: https://amp.readthedocs.io/
- A. Khorshidi & A.A. Peterson, Comput. Phys. Commun. 207, 310 (2016)
Secondary sources:
- AMP tutorials
- AmpTorch documentation
- Published catalysis applications
Confidence: VERIFIED
Verification status: ✅ VERIFIED
- Source code: OPEN (Bitbucket, GPL-3.0)
- Academic citations: >500
- Established codebase