KLIFF – KIM-based Learning-Integrated Fitting Framework¶
KLIFF is an interatomic potential fitting package that can be used to fit both physics-motivated potentials (e.g. the Stillinger-Weber potential) and machine learning potentials (e.g. neural network potential). The trained potential can be deployed with the kim-api, which is supported by major simulation codes such as LAMMPS, ASE, DL_POLY, and GULP among others.
- Frequently Asked Questions
- Package Reference
- Fork KLIFF on GitHub