Pinnwall: Heterogeneous Electrode Models from Integrating Machine Learning and Atomistic Simulation

Abstract

Electrochemical energy storage always involves the capacitive process. The prevailing electrode model used to study such polarizable electrode-electrolyte systems is the Siepmann-Sprik model, which has been recently extended to study the metallicity in the electrode model by including the Thomas-Fermi screening length. Nevertheless, a further extension to heterogeneous electrode models is required. Here, we address this challenge by integrating the atomistic machine learning code (PiNN) [1] for generating the base charge [2] and response kernel [3] and the classical molecular dynamics code (MetalWalls). This leads to the development of the PiNNwall [4] interface. Apart from the cases of chemically doped graphene and graphene oxide electrodes as shown in this study, the PiNNwall interface also allows us to probe polarized oxide surfaces in which both the proton charge and the electronic charge can coexist. Therefore, this work opens the door for modelling heterogeneous and complex electrode materials often found in energy storage systems.[1] Shao, Y.; Hellström, M.; Mitev, P. D.; Knijff, L.; Zhang, C. PiNN: A python library for building atomic neural networks of molecules and materials. J. Chem. Inf. Model. 2020, 60, 1184.[2] Knijff, L.; Zhang, C. Machine learning inference of molecular dipole moment in liquid water. Mach. Learn.: Sci. Technol. 2021, 2, 03LT03.[3] Shao, Y.; Andersson, L.; Knijff, L.; Zhang, C. Finite-field coupling via learning the charge response kernel. Electron. Struct. 2022, 4, 014012.[4] Dufils, T., Knijff, L., Shao, Y. and Zhang, C. PiNNwall: heterogeneous electrode models from integrating machine learning and atomistic simulation. arXiv preprint arXiv:2303.15307. 2023.*This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Programme (Grant Agreement No. 949012). We also acknowledge the support from the Centre for Interdisciplinary Mathematics (CIM) and the AI4Research initiative at Uppsala University.