One of the grand challenges in electrochemistry is to understand, control and design electrochemical energy materials at atomistic precision. It requires establishing quantitative relationships between the macroscopic properties such as current and voltage, and the time evolution of the corresponding bulk materials, interfaces, and interphases at the microscopic scale. This crucial information can be very difficult to extract from the top-down approach commonly used in experiments. Therefore, molecular modelling based on quantum mechanical and statistical mechanical principles becomes indispensable. tour de force in the field of molecular modelling. The 2024 Nobel Prize in Chemistry given to computational protein design and structure prediction was an appreciation of this breakthrough. For materials systems, ML has been used to accelerate molecular simulation with machine learning potentials (MLPs) which provides quantum mechanical accuracy at a fraction of the cost, to identify reaction coordinates automatically for enhanced sampling and free energy calculations, and to propose new structures with generative AI for inverse design, among many other exciting and emerging applications. A notable advancement in the development of MLPs was the introduction of equivariant features in convolutional graph neural networks (CGNNs) around 2021. The NequIP architecture is a pioneer in this advancement among many other equivariant CGNNs such as PaiNN, EGNN and NewtonNet, that appeared in the same period. It has been shown that CGNNs with equivariant features can lead to much better force and energy predictions and become more data efficient. This motivates us to incorporate equivariant features into our invariant CGNN architecture PiNet, which was first introduced and implemented in our pairwise interaction neural network package PiNN. Compared to general-purpose packages such as DeePMD and MACE, our development of PiNN has its purpose for modelling electrochemical systems from the beginning. This requires taking into consideration not only the accuracy of the Hamiltonian and the precision of sampling but also the electric boundary conditions. Keeping that in mind, we have developed special-purpose modules such as PiNetdipole for predicting dipole and charge and PiNet-χ for predicting polarizabilty and generating a charge response kernel under external bias. These have further led to the birth of PiN-Nwall that joins forces from the PiNN and MetalWalls codes for modelling heterogeneous electrodes in fully solvated electrochemical environments.