Accelerated Relaxation Engines for Optimizing to Minimum Energy Path

TL;DR

This paper introduces two novel optimization algorithms, AARE and Acc-CG, that significantly improve the efficiency of finding minimum energy paths in molecular systems, reducing computational costs compared to existing methods.

Contribution

The paper presents two new optimization algorithms, AARE and Acc-CG, specifically designed to accelerate the search for minimum energy paths in molecular potential energy surfaces.

Findings

AARE and Acc-CG outperform FIRE in test cases.

Algorithms effectively optimize energy paths on analytical potentials.

Methods reduce computational effort in reaction mechanism studies.

Abstract

In the last few decades, several novel algorithms have been designed for finding critical points on PES and the minimum energy paths connecting them. This has led to considerably improve our understanding of reaction mechanisms and kinetics of the underlying processes. These methods implicitly rely on computation of energy and forces on the PES, which are usually obtained by computationally demanding wave-function or density-function based ab initio methods. To mitigate the computational cost, efficient optimization algorithms are needed. Herein, we present two new optimization algorithms: adaptively accelerated relaxation engine (AARE), an enhanced molecular dynamics (MD) scheme, and accelerated conjugate-gradient method (Acc-CG), an improved version of the traditional conjugate gradient (CG) algorithm. We show the efficacy of these algorithms for unconstrained optimization on 2D and 4D test functions. Additionally, we also show the efficacy of these algorithms for optimizing an elastic band of images to the minimum energy path on two analytical potentials (LEPS-I and LEPS-II) and for HCN/CNH isomerization reaction. In all cases, we find that the new algorithms outperforms the standard and popular fast inertial relaxation engine (FIRE).