发表机构
The University of Hong Kong; Hong Kong Quantum AI Lab Limited; MattVerse Limited; Shenzhen Institute of Advanced Study, University of Electronic Science and Technology of China（香港大学; 香港量子人工智能实验室有限公司; 马特宇宙有限公司; 深圳先进研究院，电子科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对开放量子输运应用中现有机器学习势能的局限，提出嵌入电压偏置的E(3)等变图神经网络，在锂/水界面模拟中展现出高精度与外推性，成功捕捉电场诱导的水分子动态重取向及不对称电化学行为。
AI 中文摘要
在非平衡工作条件下模拟电化学界面对能源技术至关重要，但仍受限于从头算方法的高昂成本。当前的机器学习势能大多为均匀电场下的封闭系统设计，在开放量子输运应用中存在局限。为解决该问题，我们提出一种嵌入电压偏置的E(3)等变图神经网络，用于开放系统模拟。该方法将系统能量与力解耦为零偏置和偏置依赖项，为电极和散射区原子分配不同的向量编码以捕捉非平衡条件。在有限的离散偏置数据上训练后，模型具备高预测精度和稳健的外推迁移性。将其应用于锂/水界面时，模型成功捕捉到电场诱导的水分子动态重取向，并再现了不同电极处的不对称电化学行为。
英文摘要
Modeling electrochemical interfaces under operational non-equilibrium conditions is vital for energy technologies but remains bottlenecked by the expensive cost of ab initio methods. Current machine learning potentials, largely designed for closed systems under homogeneous electric fields, are limited in open quantum transport applications. To overcome this, we present an E(3)-equivariant graph neural network that embeds voltage bias for open-system simulations. Our approach decouples the system energy and forces into zero-bias and bias-dependent contributions, assigning distinct vector encodings to electrode and scattering-region atoms to capture non-equilibrium conditions. Trained on limited discrete bias data, the model achieves high predictive accuracy and robust extrapolation transferability. When applied to a lithium/water interface, our model successfully captures the field-induced dynamic reorientation of water molecules and reproduces asymmetric electrochemical behavior at different electrodes.
