(582ax) Analyzing Reaction Networks and Pathway Kinetics Via Metadynamics Simulations
We present a novel workflow of exploring a large, complex reaction network by using the SPRINT coordinates2 of all the atoms in the system as collective variables (CVs), and biasing them following the Parallel Bias MetaD method. We apply this framework to study the decomposition of g-ketohydroperoxide (the Korcek reaction)3 described by the PM6 Hamiltonian. Using an ensemble of simulations we are able to reconstruct a reaction network of the participating species and transition pathways that encompass steps of the documented Korcek reaction mechanism,3 as well as other fundamental chemistries.
Additionally, we discuss recent advances in sampling transition events within the infrequent MetaD framework, a variant of MetaD used to calculate transition times of rare events from biased simulations, to aid in its application to reacting systems.4 Because transition events are stochastic, many events must be sampled to get a converged estimate of the rate of transition.5 For systems with multiple competing pathways, adequately sampling all of the relevant pathways can be challenging and lead to an excessive number of simulations to be carried out. We illustrate a method that isolates individual pathways of such systems to reduce the number of simulations needed, without corrupting the recovered transition rate, and apply it to two model systems.6 Furthermore, we also discuss how the use of two generic CVs, SPRINT coordinates and collective-variable driven hyperdynamics,7 can be suitable CVs for infrequent MetaD, despite requiring a relatively low level of knowledge to define.8 We individually bias these generic CVs, and compare them to typical CV (bond distances), to recover the reaction rates of an SN2 reaction and a Diels Alder reaction. For both systems, we show that for biasing each CV leads to the same rate of reaction and energy barrier.
The poster concludes with a perspective on how these methods can be applied together to discover and then characterize the kinetics of pathways in large, complex reaction networks.
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