Generator of polynomial machine learning potentials.
Project description
A generator of polynomial machine learning potentials
Polynomial machine learning potentials
Required libraries and python modules
- numpy
- scipy
- pyyaml
- eigen3
- pybind11
- openmp (recommended)
- phonopy (if using phonon datasets and/or computing force constants)
- phono3py (if using phonon datasets and/or computing force constants)
- symfc (if computing force constants)
- spglib (optional)
- joblib (optional)
How to use pypolymlp
- Polynomial MLP development
- Property calculators
- Energy, forces on atoms, and stress tensor
- Force constants
- Elastic constants
- Equation of states
- Structural features (Polynomial invariants)
- Local geometry optimization
- Phonon properties, Quasi-harmonic approximation
- Self-consistent phonon calculations
- Utilities
- Random structure generation
- Estimation of computational costs
- Enumeration of optimal MLPs
- Compression of vasprun.xml files
- Automatic division of DFT dataset
- Atomic energies
- Python API (MLP development)
- Python API (Property calculations)
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