Diagnosing the Impact of External Electric Fields Chemical Kinetics: Application to Toluene Oxidation and Pyrolysis.

In this study we utilize biased, reactive molecular dynamics (ReaxFF MD) simulations to quantitatively assess the impact of external electric fields on the kinetics of toluene oxidation and pyrolysis. We observe that the application of a strong external electric field significantly accelerates the kinetics of toluene oxidation, while having no effect on the pyrolysis reactions. When viewed through the lens of harmonic transition state theory, this phenomenon can be ascribed to the increase in the change of entropy between the transition state and reactants for the oxidation reactions. This conclusion is further verified with a model that relates the change in entropy as a function of field strength to predict the kinetics of toluene oxidation, which accounts for the total variance in the data recovered from simulation.

Density functional tight-binding and infrequent metadynamics can capture entropic effects in intramolecular hydrogen transfer reactions.

Infrequent metadynamics uses biased simulations to estimate the unbiased kinetics of a system, facilitating the calculation of rates and barriers. Here the method is applied to study intramolecular hydrogen transfer reactions involving peroxy radicals, a class of reactions that is challenging to model due to the entropic contributions of the formation of ring structures in the transition state. Using the self-consistent charge density-functional based tight-binding (DFTB) method, we applied infrequent metadynamics to the study of four intramolecular H-transfer reactions, demonstrating that the method can qualitatively reproduce these high entropic contributions, as observed in experiments and those predicted by transition state theory modeled by higher levels of theory. We also show that infrequent metadynamics and DFTB are successful in describing the relationship between transition state ring size and kinetic coefficients (e.g., activation energies and the pre-exponential factors).

Determining energy barriers and selectivities of a multi-pathway system with infrequent metadynamics.

Estimating the transition rates and selectivity of multi-pathway systems with molecular dynamics simulations is expensive and often requires arduous sampling of many individual pathways. Developing a way to efficiently sample and characterize multi-pathway systems creates an opportunity to apply these tools to study systems that, previously, would have had a prohibitive computational cost. We present an approach that places quartic boundaries at the saddle points to isolate individual pathways without changing their observed rates, reducing the required number of events sampled and estimated rate uncertainty. In addition to rates, the selectivity between pathways is also accurately predicted as well. To further reduce the computational cost of the analysis, we have paired this approach with the infrequent metadynamics method. The method is demonstrated on model systems and stiffened alanine dipeptide. Furthermore, we present an appropriate method for recovering the energy barriers of specific transition paths by taking the slope of an Arrhenius plot generated from the infrequent metadynamics results at various temperatures. We also compare this method against another previously published literature to demonstrate its superior performance. In the future, these methods can be used in a variety of contexts where competing escape pathways with different barriers are relevant.

Lifting the Curse of Dimensionality on Enhanced Sampling of Reaction Networks with Parallel Bias Metadynamics.

A common challenge to applying metadynamics to the study of complex systems is selecting the proper collective variables to bias. The advent of generic collective variables, specifically social permutation invariant (SPRINT) coordinates, has helped to address this challenge by reducing the level of a priori knowledge required to just basic chemical fundamentals. However, the efficiency of biasing SPRINT coordinates can be severely handicapped by the high dimensionality of the bias potential. Here, we circumvent this deficiency by biasing SPRINT coordinates using the parallel bias metadynamics framework. We demonstrate the efficacy of this method to efficiently explore a complex system, without any prior knowledge about transition pathways, by applying it to study the decomposition of γ-ketohydroperoxide and generating a comprehensive reaction network of relevant pathways. The reduction in both computational cost and chemical intuition makes this method a promising option for studying complex reacting systems.

Biasing Smarter, Not Harder, by Partitioning Collective Variables into Families in Parallel Bias Metadynamics.

Molecular simulations of systems with multiple copies of identical atoms or molecules may require the biasing of numerous, degenerate collective variables (CVs) to accelerate sampling. Recently, a variation of metadynamics (MetaD) named parallel bias metadynamics (PBMetaD) has been shown to make biasing of many CVs more tractable. We extended the PBMetaD scheme so that it partitions degenerate CVs into families that share the same bias potential, consequently expediting convergence of the free-energy landscape. We tested our method, named parallel bias metadynamics with partitioned families, on 3, 21, and 78 CV systems and obtained an approximately proportional increase in convergence speed compared to standard PBMetaD.

Assessing Generic Collective Variables for Determining Reaction Rates in Metadynamics Simulations.

A persistent challenge in using the metadynamics method is deciding which degrees of freedom, or collective variables, should be biased because these selections are not obvious and require intuition about the system being studied. There are, however, collective variables, which can be constructed with only basic knowledge about the system studied, that provide an opportunity to alleviate this issue. We simulated two different reacting systems where two types of such collective variables (SPRINT coordinates and the collective variable-driven hyperdynamics method) were biased following the infrequent metadynamics method in order to recover the rates of reactions. We demonstrate that both generic collective variables are capable of reproducing the reaction rates of both systems and can enhance the efficiency of the simulation when compared to typical collective variables.