Nonequilibrium Dynamics of Proton-Coupled Electron Transfer in Proton Wires: Concerted but Asynchronous Mechanisms

Abstract

The coupling between electrons and protons and the long-range transport of protons play important roles throughout biology. Biomimetic systems derived from benzimidazole-phenol (BIP) constructs have been designed to undergo proton-coupled electron transfer (PCET) upon electrochemical or photochemical oxidation. Moreover, these systems can transport protons along hydrogen-bonded networks or proton wires through multiproton PCET. Herein, the nonequilibrium dynamics of both single and double proton transfer in BIP molecules initiated by oxidation are investigated with first-principles molecular dynamics simulations. Although these processes are concerted in that no thermodynamically stable intermediate is observed, the simulations predict that they are predominantly asynchronous on the ultrafast time scale. For both systems, the first proton transfer typically occurs ∼100 fs after electron transfer. For the double proton transfer system, typically the second proton transfer occurs hundreds of femtoseconds after the initial proton transfer. A machine learning algorithm was used to identify the key molecular vibrational modes essential for proton transfer: a slow, in-plane bending mode that dominates the overall inner-sphere reorganization, the proton donor–acceptor motion that leads to vibrational coherence, and the faster donor–hydrogen stretching mode. The asynchronous double proton transfer mechanism can be understood in terms of a significant mode corresponding to the two anticorrelated proton donor–acceptor motions, typically decreasing only one donor–acceptor distance at a time. Although these PCET processes appear concerted on the time scale of typical electrochemical experiments, attaching these BIP constructs to photosensitizers may enable the detection of the asynchronicity of the electron and multiple proton transfers with ultrafast two-dimensional spectroscopy. Understanding the fundamental PCET mechanisms at this level will guide the design of PCET systems for catalysis and energy conversion processes.

The Sabatier Principle in Electrocatalysis: Basics, Limitations, and Extensions

“…To date, the energetics of SPET pathways has been assessed theoretically for some molecular catalysts, and it has been shown that charged intermediates are indeed preferred during reactions such as CO 2 reduction (Göttle and Koper, 2017) or the OER (Craig et al, 2019). Thorough treatment of CPET and SPET pathways by quantum chemical considerations can be found in the works of Hammes-Schiffer and coworkers, who have developed computational protocols for the calculation of concerted or decoupled proton-electron transfers for biological enzymes and bio-inspired molecules (Goings and Hammes-Schiffer, 2020;Sayfutyarova and Hammes-Schiffer, 2020;Warburton et al, 2020). Although direct calculation of charged intermediates using DFT tends to be avoided due to their large errors, moving beyond the conventional picture of uncharged intermediate states within the CHE model is of particular importance to the electrocatalysis community.…”

Role of Intact Hydrogen-Bond Networks in Multiproton-Coupled Electron Transfer

“…27 Nonequilibrium first-principles molecular dynamics simulations have provided further insights into this type of concerted but asynchronous E2PT process and have identified the crucial vibrational modes for proton transfer. 28 Considering that the pK a of the pyridyl group is in the same range as the benzimidazole moiety, 29,30 pyridyl-BIPs are attractive candidates to evaluate whether ground-state PCET processes can indeed occur. Both the neutral and protonated forms of the pyridyl moiety in the ground state have characteristic and different IR transitions; 31,32 thus, arrival of the proton in a PCET process can be detected by using a variety of techniques including IRSEC.…”

Reinforcement Learning Configuration Interaction

“…For example, machine learning has been used to design new materials, 34 to predict protein structure, 35 ground 36 and excited 37 state molecular properties, and even aid in the interpretation of complex molecular dynamics trajectories. 38,39 For the selected CI problem, machine learning has been used to predict important configurations using supervised learning on data generated on-the-fly. 40,41 This enables more accurate potential energy surfaces with fewer iterations as compared to a stochastic sCI approach.…”