Assessment and optimization of the fast inertial relaxation engine (FIRE) for energy minimization in atomistic simulations and its implementation in LAMMPS

摘要

In atomistic simulations, pseudo-dynamical relaxation schemes often exhibit better performance and accuracy in finding local minima than line-search-based descent algorithms like steepest descent or conjugate gradient. Here, an improved version of the fast inertial relaxation engine (FIRE ) and its implementation within the open-source atomistic simulation code LAMMPS is presented. It is shown that the correct choice of time integration scheme and minimization parameters is crucial for the performance of FIRE.