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 in the molecular simulation of polarizable electrode–electrolyte systems is the Siepmann–Sprik model developed for perfect metal electrodes. This model has been recently extended to study the metallicity in the electrode by including the Thomas–Fermi screening length. Nevertheless, a further extension to heterogeneous electrode models requires introducing chemical specificity, which does not have any analytical recipes. Here, we address this challenge by integrating the atomistic machine learning code (PiNN) for generating the base charge and response kernel and the classical molecular dynamics code (MetalWalls) dedicated to the modeling of electrochemical systems, and this leads to the development of the PiNNwall 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 modeling heterogeneous and complex electrode materials often found in energy storage systems.

Understanding Sorption of Aqueous Electrolytes in Porous Carbon by NMR Spectroscopy

“…We ascribe the increased ionophilicity to the protonation of the basic groups, which then in turn results in an increased uptake of the charge-compensating TFSI – groups, with the total uptake of H 3 O + (0.8–1.2 mmol g –1 , depending on pH and salt concentration) being of the same order as the increased TFSI – uptake. The ionophilic nature of carbon in acidic electrolytes has implications on the charge storage mechanisms in aqueous electrolyte-based supercapacitor devices and is consistent with the higher capacitance measured for YP-50F in the acidic LiTFSI electrolyte of 141 F g –1 compared to the capacitances of 96 and 83 F g –1 in neutral and basic LiTFSI electrolytes, respectively (Figure ), with these values being similar to the literature value of 85 F g –1 for YP-50F in 1 M KOH. , Indeed, a recent theoretical paper has highlighted the additional capacitance that results from the protonation of basic groups on graphene sheets …”

“…38,39 Indeed, a recent theoretical paper has highlighted the additional capacitance that results from the protonation of basic groups on graphene sheets. 40 The clear ionophilic nature of YP-50F is in contrast to earlier work by Cervini et al on their PDCs; this system is ionophobic with respect to hydrated Li + and Na + ions in aqueous LiCl and NaCl electrolytes, leading to a lower in-pore than ex-pore ion concentration. 37 We suggest that the difference between PDCs and YP-50F lies in the nature and quantity of (internal) surface groups on PDCs, motivating further studies of the functional groups in this class of carbon.…”

Modeling of Nanomaterials for Supercapacitors: Beyond Carbon Electrodes

“…Despite its success, the native version of CPM (see Section ) for describing polarizable electrode are not capable to account for electrode heterogeneity, both in terms of chemical composition as well as local geometry. ,,− An effort was made to remedy this recently through the use of machine learning (ML)-methods by the introduction of the PiNNwall interface . PiNNwall interfaces the molecular dynamics code MetalWalls, , specifically designed to simulate electrochemical systems, with two machine learning models which are part of the graph convolution neutral network code PiNN: PiNet-χ and PiNet-dipole .…”

“…V PZFC is identified when the electrode charge becomes zero. Reprinted with permission under a Creative Commons CC BY License from ref . Copyright 2023 American Chemical Society.…”