Stable PAIPAI v2.0 release.
PAIPAI is a Monte Carlo structure-search and finite-temperature sampling code for defective high-entropy alloys using machine-learning interatomic potentials.
This release includes:
- MLIP-assisted search-mode Monte Carlo sampling
- finite-temperature Monte Carlo workflows
- metal and interstitial move sets
- accepted-state post-processing tools, including Warren-Cowley SRO analysis and MC process packaging
- example CPU/GPU and SLURM scripts
If you use PAIPAI in academic work, please cite the associated publication listed in the README.