Assigning atom types and deriving FF parameters are typical atom-level tasks. Building on the notion of directed bonds, we used a Directed Graph Attention neTworks (D-GATs) model. In contrast to other ML-based molecular processing models, D-GATs exhibit a remarkable ability to discern local chemical environments and eliminate unnecessary message flow. They have consistently outperformed state-of-the-art benchmarks in 13 out of 15 molecular property prediction tasks. To enhance the robustness, we employ the Smooth Maximum Unit (SMU) as an activation function. SMU offers a smooth approximation to the entire Maxout family, including ReLU, Leaky ReLU, and their variants. Our goal is to predict a set of parameters that can bring molecular dynamic simulation results closer to ab-initio data. The molecular potential energy surface is highly sensitive to these predicted parameters. Our test demonstrated that the discontinuity in the Maxout function can hinder the convergence of the models' loss. To ensure compatibility with GAFF, we focused on compounds composed of C, N, O, H, S, P, F, Cl, Br, and I. Using the RDKit package, we extracted fundamental atomic and bond features (see Table ). These features, in conjunction with the molecular graph represented in Lewis structure, were then input into the GB-FFs model. For more information about the accuracy of the predicted atom types and features, refer to the section 'Recovering atom types in GAFF' in Supplementary Information. The model's outputs include atomic and bond representations. For this article, we specifically employ the directed bond representations to incorporate chemical information and the atomic representations for predicting FF parameters (as illustrated in Figure )." As depicted in Figure , the Large Layers consist of 3 interaction layers with a model dimension (D h ) of 512. They play a central role in detecting chemical environments and forming atomic representations. The Small Layers, on the other hand, employ 4 interaction layers with a model dimension (D h ) of 128 and four attention heads. These layers are primarily used for embedding initialization and demand minimal computational resources.