Computational understanding of the liquid–electrode interface faces challenges in efficiently incorporating reactive force fields and electrostatic potentials within reasonable computational costs. Although universal neural network potentials (UNNPs), representing pretrained machine learning interatomic potentials, are emerging, current UNNP models lack explicit treatment of Coulomb potentials, and methods for integrating additional charges on the electrode remain to be established. We propose a method to analyze liquid–electrode interfaces by integrating a UNNP, known as the preferred potential, with Coulomb potentials using the ONIOM method. This approach extends the applicability of UNNPs to electrode–liquid interface systems. Through molecular dynamics simulations of graphene–water and graphene oxide (GO)–water interfaces, we demonstrate the effectiveness of our method. Our findings emphasize the necessity of incorporating long-range Coulomb potentials into the water potential to accurately describe water polarization at the interface. Furthermore, we observe that functional groups on the GO electrode influence both polarization and capacitance.

中文翻译：

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通过将库仑相互作用集成到通用神经网络电位中来研究液电极界面的分子动力学


对液电极界面的计算理解面临着以合理的计算成本有效整合反作用力场和静电势的挑战。尽管代表预训练机器学习原子间电位的通用神经网络电位 （UNNP） 正在出现，但当前的 UNNP 模型缺乏对库仑电位的明确处理，并且在电极上集成额外电荷的方法仍有待建立。我们提出了一种通过使用 ONIOM 方法将 UNNP（称为首选电位）与库仑电位集成来分析液电极界面的方法。这种方法将 UNNP 的适用性扩展到电极-液体界面系统。通过石墨烯-水和氧化石墨烯 （GO）-水界面的分子动力学模拟，我们证明了我们方法的有效性。我们的研究结果强调了将长程库仑势纳入水势以准确描述界面处水极化的必要性。此外，我们观察到 GO 电极上的官能团会影响极化和电容。