(582ax) Analyzing Reaction Networks and Pathway Kinetics Via Metadynamics Simulations

Authors: 
Fu, C., University of Washington
Pfaendtner, J., University of Washington
Investigating reacting systems can be challenging due to the large number of species and pathways involved in these networks. A common obstacle to computational studies of reacting systems is forming a comprehensive scheme of all the relevant species and transition states. Molecular dynamics (MD) offers the ability to characterize the thermodynamics and kinetics of the different states and pathways involved in a particular system at high resolution. However, the computational cost associated with MD simulations often limits its application in this area. While a variety enhanced sampling methods, such as metadynamics (MetaD),1 have been developed to reduce the computational cost of simulations, these methods typically require identification of a reaction coordinate of relevant degrees of freedom for transitions. In this poster, we highlight developments to the MetaD family of methods that reduce the level of chemical insight required and expedite sampling of transition paths, making this method well-suited for studying complex reaction systems.

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.

References:

(1) Valsson, O.; Parrinello, M. Variational Approach to Enhanced Sampling and Free Energy Calculations. Phys. Rev. Lett. 2014, 113 (9), 1–5.

(2) Pietrucci, F.; Andreoni, W. Graph Theory Meets Ab Initio Molecular Dynamics: Atomic Structures and Transformations at the Nanoscale. Phys. Rev. Lett. 2011, 107 (8), 85504.

(3) Jalan, A.; Alecu, I. M.; Aguilera-Iparraguirre, J.; Merchant, S. S.; Yang, K. R.; Merhcant, S. S.; Truhlar, D. G.; Green, W. H. New Pathways for Formation of Acids and Carbonyl Products in Low Temperature Oxidation. J. Am. Chem. Soc. 2013, 135, 11100–11114.

(4) Tiwary, P.; Parrinello, M. From Metadynamics to Dynamics. Phys. Rev. Lett. 2013, 111 (23), 230602.

(5) Salvalaglio, M.; Tiwary, P.; Parrinello, M. Assessing the Reliability of the Dynamics Reconstructed from Metadynamics. J. Chem. Theory Comput. 2014, 10 (4), 1420–1425.

(6) Fu, C. D.; L. Oliveira, L. F.; Pfaendtner, J. Determining Energy Barriers and Selectivities of a Multi-Pathway System with Infrequent Metadynamics. J. Chem. Phys. 2017, 146 (1), 14108.

(7) Bal, K. M.; Neyts, E. C. Merging Metadynamics into Hyperdynamics: Accelerated Molecular Simulations Reaching Time Scales from Microseconds to Seconds. J. Chem. Theory Comput. 2015, 11 (10), 4545–4554.

(8) Fu, C. D.; Oliveira, L. F. L.; Pfaendtner, J. Assessing Generic Collective Variables for Determining Reaction Rates in Metadynamics Simulations. J. Chem. Theory Comput. 2017, acs.jctc.7b00038.