A Machine-Learning Accelerated Grand Canonical Sampling Framework for Nuclear Quantum Effects in Constant Potential Electrochemistry

TL;DR

This paper introduces a machine learning-accelerated grand canonical sampling framework that explicitly incorporates nuclear quantum effects into simulations of proton-coupled electron transfer, revealing their significant impact on reaction energetics.

Contribution

It develops a unified computational approach combining grand canonical sampling with machine learning force fields to accurately simulate nuclear quantum effects in electrochemical PCET processes.

Findings

NQEs significantly lower activation energies in PCET.

Wave-like quantum behavior facilitates proton tunneling.

NQEs influence broader energy conversion reactions.

Abstract

Proton-coupled electron transfer (PCET) is the key step for energy conversion in electrocatalysis. Atomic-scale simulation acts as an indispensable tool to provide a microscopic understanding of PCET. However, consideration of the quantum nature of transferring protons under an exact grand canonical (GC) constant potential condition is a great challenge for theoretical electrocatalysis. Here, we develop a unified computational framework to explicitly treat nuclear quantum effects (NQEs) by a sufficient GC sampling, further assisted by a machine learning force field adapted for electrochemical conditions. Our work demonstrates a non-negligible impact of NQEs on PCET simulations for hydrogen evolution reaction at room temperature, and provides a physical picture that wave-like quantum characteristic of the transferring protons facilitates the particles to tunnel through classical barriers in PCET paths, leading to a remarkable activation energy reduction compared to classical simulations. Moreover, the physical insight of NQEs may reshape our fundamental understanding of other types of PCET reactions in broader scenarios of energy conversion processes.