Advances in the computational efficiency and scalability of ab-initio methods have also provided new opportunities to enhance the transferability and accuracy of FFs by building larger and more accurate databases. 39,40 A question arises: can ML be used to enhance the prediction of FF parameters by training on ab-initio databases? Recently, the Espaloma model combined Graph Neural Networks (GNNs) and automatic differentiation to predict FF parameters. 41 By focusing on intramolecular interactions, they demonstrated that GNNs can effectively predict FF parameters based on potential energies.