Zhu Jia-Xin, Cheng Jun
Xiamen University, State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, Xiamen 361005, China.
Xiamen University, Xiamen University, State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, Xiamen 361005, China and Laboratory of AI for Electrochemistry (AI4EC), IKKEM, Xiamen 361005, China and Institute of Artificial Intelligence, Xiamen 361005, China.
Phys Rev Lett. 2025 Jul 2;135(1):018003. doi: 10.1103/48ct-3jxm.
Understanding electrochemical interfaces at a microscopic level is essential for elucidating important electrochemical processes in electrocatalysis, batteries, and corrosion. While ab initio simulations have provided valuable insights into model systems, the high computational cost limits their use in tackling complex systems of relevance to practical applications. Machine learning potentials offer a solution, but their application in electrochemistry remains challenging due to the difficulty in treating the dielectric response of electronic conductors and insulators simultaneously. In this Letter, we propose a hybrid framework of machine learning potentials that is capable of simulating metal-electrolyte interfaces by unifying the interfacial dielectric response accounting for local electronic polarization in electrolytes and nonlocal charge transfer in metal electrodes. We validate our method by reproducing the bell-shaped differential Helmholtz capacitance at the Pt(111)-electrolyte interface. Furthermore, we apply the machine learning potential to calculate the dielectric profile at the interface, providing new insights into electronic polarization effects. Our Letter lays the foundation for atomistic modeling of complex, realistic electrochemical interfaces using machine learning potential at ab initio accuracy.
在微观层面理解电化学界面对于阐明电催化、电池和腐蚀等重要电化学过程至关重要。虽然从头算模拟为模型系统提供了有价值的见解，但高计算成本限制了它们在处理与实际应用相关的复杂系统中的应用。机器学习势能提供了一种解决方案，但由于难以同时处理电子导体和绝缘体的介电响应，它们在电化学中的应用仍然具有挑战性。在本信函中，我们提出了一种机器学习势能的混合框架，该框架能够通过统一考虑电解质中局部电子极化和金属电极中非局部电荷转移的界面介电响应来模拟金属 - 电解质界面。我们通过重现Pt(111) - 电解质界面处的钟形微分亥姆霍兹电容来验证我们的方法。此外，我们应用机器学习势能来计算界面处的介电分布，为电子极化效应提供了新的见解。我们的信函为使用具有从头算精度的机器学习势能对复杂、实际的电化学界面进行原子尺度建模奠定了基础。
