2020
DOI: 10.1016/j.commatsci.2020.109584
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Assessment and optimization of the fast inertial relaxation engine (fire) for energy minimization in atomistic simulations and its implementation in lammps

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 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.

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Cited by 144 publications
(72 citation statements)
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“…The atomic interaction was modeled using an embedded-atom-method (EAM) potential for Ag. 85 The wires were equilibrated at 600 K for 10 ps and then quenched by FIRE 86 to an equilibrium configuration followed by a thermal equilibration at 300 K for 200 ps. Uniaxial tensile tests were performed on AgNW ( d = 15.2 nm, length l = 450.9 nm) with periodic boundary conditions (PBC) along the wire axis.…”
Section: Methodsmentioning
confidence: 99%
“…The atomic interaction was modeled using an embedded-atom-method (EAM) potential for Ag. 85 The wires were equilibrated at 600 K for 10 ps and then quenched by FIRE 86 to an equilibrium configuration followed by a thermal equilibration at 300 K for 200 ps. Uniaxial tensile tests were performed on AgNW ( d = 15.2 nm, length l = 450.9 nm) with periodic boundary conditions (PBC) along the wire axis.…”
Section: Methodsmentioning
confidence: 99%
“…The interatomic interactions were modeled using the modified embedded atom method (MEAM) potential from Kim et al [27] published in 2015. Energy minimization was performed using a combination of the conjugate gradient (CG) algorithm with box relaxation and the FIRE algorithm [51,52] until the force norm was below 10 −8 eV/Å. The point defects were introduced by removing an atom (vacancy) or swapping atom types (anti-site) in the C14 sample with periodic boundary conditions (PBCs) in all directions.…”
Section: Simulation Methodsmentioning
confidence: 99%
“…For full minimization of the discretized density field we start with an initial guess (based, e.g., on minimized Gaussian profiles or the density field minimized at some other thermodynamic parameters) and use the fast inertial relaxation engine algorithm [19]. This is a type of gradient descent with inertia and we find it reliably converges in typically a few thousand iterations for the case of the solid (and an order of magnitude faster for the fluid).…”
Section: B Full Minimizationmentioning
confidence: 99%