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

## TL;DR

This paper presents an improved FIRE algorithm integrated into LAMMPS, demonstrating enhanced efficiency in energy minimization for atomistic simulations through optimal parameter selection and time integration schemes.

## Contribution

An improved FIRE method with implementation details in LAMMPS, highlighting the importance of parameter tuning for better performance in atomistic energy minimization.

## Key findings

- FIRE outperforms traditional line-search methods in certain scenarios.
- Correct parameter choice significantly improves minimization efficiency.
- Implementation in LAMMPS makes the method accessible for widespread use.

## Abstract

In atomistic simulations, pseudo-dynamics 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 code LAMMPS is presented. It is shown that the correct choice of time integration scheme and minimization parameters is crucial for performance.