Tracking the Delocalized Proton in Concerted Proton Transfer in Bulk Water

Abstract

A solvated proton in water is often characterized as a charge or structural defect, and it is important to track its evolution on-the-fly in certain dynamics simulations. Previously, we introduced the proton indicator, a pseudo-atom, whose position approximates the location of the excess proton modeled as a structural defect. The proton indicator generally yields a smooth trajectory of a hydrated proton diffusing in aqueous solutions, including in the events of stepwise proton transfer via the Grotthuss mechanism; however, the proton indicator did not perform well in the events of concerted proton transfer, for which it occasionally yielded large position displacements between two successive time steps. To overcome this hurdle, we develop a new algorithm of a proton indicator with greatly enhanced performance for concerted proton transfer in bulk water. A protocol is proposed to exhaustively explore the hydrogen-bonding network of the water wires over which the excess proton is delocalized and to properly account for the contributions of the water molecules in this network as the geometry evolves. The new proton indicator (called Indicator 2.0) is assessed in dynamics simulations of an excess proton in bulk water and in specially constructed model systems of more complex architectures. The results demonstrate that the new indicator yields a smooth trajectory in both stepwise and concerted proton transfers.

Adaptive-Partitioning Multilayer Dynamics Simulations: 2. Implementations of the Permuted and Interpolated Adaptive-Partitioning Gradients

“…Eigen cation is one of the common forms of a hydrated proton, which is often considered to be delocalized over the hydronium ion and its solvation shell. The diffusion of a hydrated proton is dominated by the Grotthuss mechanism through reorganizing the network of hydrogen and covalent bonds of the water molecules. , Thus, the study of hydrate protons is very important and has been the theme of a number of adaptive QM/MM simulations. ,,,,,,,− Here, similar to what we did in the water clusters, we gradually pulled one water molecule away from the solvation shell of the hydronium ion while keeping the other atoms fixed. The model and results are detailed in Figures S30–S32 in the Supporting Information.…”

“…[We note that the idea of dynamically relocated interlayer boundaries had also been proposed in the ONIOM-XS (ONIOM with exchanging solvents) algorithm, , but only two-layer QM/MM descriptions were actually employed.] With its flexibility in adjusting the interlayer boundaries as needed, adaptive QM/MM has been successfully applied to the dynamics simulations of diffusive model systems, such as ions (including protons) migrating in a bulk solution ,,,− or through channel proteins , as well as solvent molecules exchanged between a protein active site and the bulk solution . The moving boundaries would allow one to characterize the given ion and its various solvation shells with hierarchically varying accuracy and efficiency, with the contents of these solvation shells automatically updated over time.…”

In Silico Demonstration of Fast Anhydrous Proton Conduction on Graphanol

“…We developed a modified center of excess charge (CEC) method, based on previous work by Li and Swanson, to estimate the diffusion of protons on charged graphanol. Similar methods, such as the proton indicator, , have also been developed to model excess protons in bulk water using only geometric information. Our CEC method is a classical surrogate for the position of the proton that tracks the position of O atoms with two H nearest neighbors, thus estimating the location of charge centers ( r CEC ).…”

Tell Machine Learning Potentials What They Are Needed For: Simulation-Oriented Training Exemplified for Glycine

“…The original form without parameter a was introduced for a different problem in the literature. 74 Parameter a gives us the additional flexibility of controlling how quickly the weighting function decreases to 0 as relative energy ΔE increases (Figure 2). The larger a is, the more quickly the weighting function decreases and the less significant the high-energy data points are.…”

“…This function was the best among the many possible choices we tried. The original form without parameter a was introduced for a different problem in the literature . Parameter a gives us the additional flexibility of controlling how quickly the weighting function decreases to 0 as relative energy Δ E increases (Figure ).…”