Advancing Force Fields Parameterization: A Directed Graph Attention Networks Approach

27 May 2024, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


Force Fields (FFs) are an established tool for simulating large and complex molecular systems. However, parametrizing FFs is a challenging and time-consuming task that relies on empirical heuristics, experimental data, and computational data. Recent efforts aim to automate the assignment of FF parameters using pre-existing databases and on-the-fly ab-initio data. In this study, we propose a Graph-Based Force Fields (GB-FFs) model to directly derive parameters for the Generalized Amber Force Field (GAFF) from chemical environments and research into the influence of functional forms. Our end-to-end parameterization approach eliminates the need for expert-defined procedures and enhances the accuracy and transferability of GAFF across a broader range of molecular complexes. The GB-FFs model, which is only grounded on ab initio data, is implemented in the highly parallel Tinker-HP GPU package. Simulation results are compared to the original GAFF parameterization and validated on various experimentally and computationally derived properties, including free energies.


machine learning
force field
force field parametrization
free energy


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