However, the accuracy of MLPs depends on the quality of the training data and the architecture of the ML model, which can limit their transferability. Moreover, MLP models are often difficult to interpret, posing challenges in identifying and understanding the underlying physics and chemistry of the systems under study. To address this, one strategy involves constructing hybrid models that combine MLPs and FFs, bridging the gap between the short-range accuracy of quantum mechanics and the computational efficiency of FFs. However, FFs remain widely used across various areas due to their computational efficiency and such hybrid MLP FF strategies still requires an accurate FF parameterization. Parameterizing a FF is a challenging task since its accuracy and transferability heavily depend on the quality of its parameters. This process is time-consuming, often taking years, as it relies on empirical heuristics, experimental data as well as computational data. FFs have established robust parameterization procedures, such as Antechamber for GAFF 8 and poltype2 for AMOEBA. Additionally, these FFs rely on empirically-assigned atom types and atom classes, to assign parameters (e.g., bonds, angles) that are specific to the local topology. To enhance the generalization and reliability of FFs, one tendency is to expand the atom type space. However, this leads to an increasing number of possible valence compositions, introducing significant complexity in the parameter fitting process. Moreover, even with modern parameter optimization frameworks 38 and sufficient data, FF parameters defined by fixed atom types can sometimes suffer from low transferability.