Ab initio molecular dynamics (AIMD) has been proven to be a powerful tool to study complex chemical systems such as electrochemical interfaces [1]. Insisting on rigorous treatment of electrochemical interfaces both quantum and statistical mechanically, not only has AIMD helped resolve the microscopic structures of electric double layers under bias potential [2], often in collaboration with in situ spectroscopic characterization, but also demonstrated that water adsorption on metal electrodes like Pt has significant impact on dielectric properties of the interfaces, leading to negative capacitive response and thus bell-shaped differential capacitances of Helmholtz layers [3].
The high computational cost of AIMD however limits its application to small model systems consisting of hundreds of atoms at timescale of tens of ps. While, the latest development of AI accelerated AIMD (AI2MD) significantly increases the size and timescale, showing great promise for in situ modeling of realistic electrochemical systems. The prerequisite is that the machine learning potential (MLP), often short-sighted in the common implementations, should be able to accurately capture long-range electrostatics, as well as both local and non-local dielectric responses of electrode-electrolyte interfaces. In this talk, I will present our recent effort in developing such an electrochemical MLP (ec-MLP) that utilizes a hybrid scheme combining Wannier localization and polarizable electrode method to account for polarization of the interface [4]. The accuracy of the ec-MLP has been validated against AIMD simulation of electrified Pt water interface, reproducing the bell-shaped differential capacitive curve.
References:
[1] J.-B. Le, M. Iannuzzi, A. Cuesta, J. Cheng*, Determining potentials of zero charge of metal vs standard hydrogen electrode from DFTMD, Phys. Rev. Lett., 2017, 119, 16801.
[2] C.-Y. Li#, J.-B. Le#, Y.-H. Wang, S. Chen, Z.-L. Yang, J.-F. Li*, J. Cheng*, Z.-Q. Tian, In situ probing electrified interfacial water structures at atomically flat surfaces., Nat. Mater., 2019, 18, 697-701.
[3] J.-B. Le, Q.-Y. Fan, J.-Q. Li, J. Cheng*, Molecular origin of negative component of Helmholtz capacitance at electrified Pt(111)/water interface, Sci. Adv., 2020, 41, eabb1219.
[4] J.-X. Zhu*, J. Cheng*, Machine Learning Potential for Electrochemical Interfaces with Hybrid Representation of Dielectric Response, Phys. Rev. Lett. 2025, 135, 018003.
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