@Article{D5RE00180C,
author ="Simon, Sandra Liz and Kaistha, Nitin and Agarwal, Vishal",
title  ="Accelerated relaxation engines for optimizing to a minimum energy path",
journal  ="React. Chem. Eng.",
year  ="2025",
volume  ="10",
issue  ="10",
pages  ="2285-2299",
publisher  ="The Royal Society of Chemistry",
doi  ="10.1039/D5RE00180C",
url  ="http://dx.doi.org/10.1039/D5RE00180C",
abstract  ={"In the last few decades{,} several novel algorithms have been designed for finding critical points on a potential energy surface (PES) and the minimum energy paths connecting them. This has led to a considerable improvement in our understanding of reaction mechanisms and the kinetics of the underlying processes. These methods implicitly rely on computation of energy and forces on the PES{,} which are usually obtained via 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 first-order optimization algorithms: the adaptively accelerated relaxation engine (AARE){,} an enhanced molecular dynamics (MD) scheme{,} and the accelerated conjugate-gradient (Acc-CG) method{,} an improved version of the traditional conjugate gradient (CG) algorithm. We show the efficacy of these algorithms for unconstrained optimization on 2-dimensional and 4-dimensional test functions. Additionally{,} we also show the efficacy of these algorithms for optimizing an elastic band of images to the minimum energy path on 2-dimensional analytical potentials{,} heptamer island transitions{,} the HCN/CNH isomerization reaction{,} and the keto–enol tautomerization reaction."}
}