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
- 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)
- openmp (recommended)
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)
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
pypolymlp-0.2.1.tar.gz
(27.4 MB
view details)
File details
Details for the file pypolymlp-0.2.1.tar.gz
.
File metadata
- Download URL: pypolymlp-0.2.1.tar.gz
- Upload date:
- Size: 27.4 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.11.9
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 08db7ed9cda94dfb22d769bf2edec58f815ef9d448e163f22d8103d9e864ddb2 |
|
MD5 | 11916fdbf9992982e58efe909af22d79 |
|
BLAKE2b-256 | 55c54d19b6d6af3034a7d81517af4636b5b05bb07696a141eb031bf34726e8f2 |