SEAKMC_py is a Python software package developed based on the self-evolving atomistic kinetic Monte Carlo (SEAKMC) [1] [2]. It includes the recent developments of scale normal coordinates (SNC) [3], angle check in saddle point search (SPS) [4], and mean rate method (MRM) to handle the low energy barrier (LEB). In addition, SEAKMC_py has implemented the point group (PG) symmetry operations in saddle point sampling, prefactor calculations based on the harmonic theory. Moreover, SEAKMC_py allows loading the defect bank (DB) from an external database, recycled saddle points from the previous KMC steps, and the customer defined saddle points to guide the SPS. More importantly, SEAKMC_py has first introduced a dynamic and automated method to identify the defects on the fly and the dynamic active volume (DAV) for SPS. This DAV can significantly improve the efficiency of the SPS in both time cost and the probability of finding relevant saddle points, which is essential for sampling the potential energy landscape and building the event table for KMC simulations.

SEAKMC_py itself does NOT include any energy or force evaluator. The python wrapper of LAMMPS (pyLAMMPS) is fully implemented in SEAKMC_py.

SEAKMC_py requires python 3.0 or above, anaconda 4.0 or above, pymatgen and mpi4py.


Tao Liang and Haixuan Xu, Saddle point search with the dynamic active volume, Computational Materials Science, 228(2023) 112354.





  1. Xu, H., Y.N. Osetsky, and R.E. Stoller, Simulating complex atomistic processes: On-the-fly kinetic Monte Carlo scheme  with selective active volumes. Physical Review B, 2011. 84: p. 132103.
  2. Xu, H., Y.N. Osetsky, and R.E. Stoller, Self-evolving atomistic kinetic Monte Carlo: fundamentals and applications. Journal of Physics: Condensed Matter, 2012. 24: p. 375402.
  3. Hayakawa, S. and H. Xu, Saddle point sampling using scaled normal coordinates. Computational Materials Science, 2021. 200: p. 110785.
  4. Hayakawa, S., et al., Atomistic modeling of meso-timescale processes with SEAKMC: A perspective and recent developments. Computational materials science, 2021. 194: p. 110390.