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.

If you find KLIFF useful in your research, please cite:

  title   = {{KLIFF}: A framework to develop physics-based and machine learning interatomic potentials},
  author  = {Mingjian Wen and Yaser Afshar and Ryan S. Elliott and Ellad B. Tadmor},
  journal = {Computer Physics Communications},
  volume  = {272},
  pages   = {108218},
  year    = {2022},
  doi     = {10.1016/j.cpc.2021.108218},

Indices and tables#