Abstract
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.
Highlights
Numerical optimization [1,2] is of utmost importance in almost every field of science and engineering
The computationally most expensive task in atomistic simulations is typically the calculation of the interatomic forces, the number of force evaluations is used for comparing minimizer performances
The performance in optimizing a configuration is determined by the ratio of the number of forces evaluations required by Conjugated Gradient (CG) or fast inertial relaxation engine (FIRE) to reach the threshold, over the number of forces evaluations required by FIRE 2.0
Summary
Numerical optimization [1,2] is of utmost importance in almost every field of science and engineering. Other uses of energy minimization methods in atomistic simulations include the search for transition states, e.g., by the nudged-elastic-band (NEB) method [7], or the detection of transitions in accelerated MD methods like parallel-replica dynamics or hyperdynamics [8]. Most atomistic simulation packages like LAMMPS [9], GROMACS [10], IMD [11], DL_POLY [12], EON [13] or ASE [14] implement line-search-based descent algorithms like Steepest Descent (SD) or Conjugated Gradient (CG), as well as damped-dynamics methods like Microconvergence [15], Quickmin [16] and the Fast Inertial Relaxation Engine (FIRE) [17]. We suggest a modification of the FIRE algorithm to improve its efficiency and describe our implementation of this modified version FIRE 2.0 in the atomistic simulation code LAMMPS [9]
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