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Polymer simulation with both accuracy and efficiency is a challenging task.Machine learning (ML) forcefields have been developed to achieve both theaccuracy of ab initio methods and the efficiency of empirical force fields.However, existing ML force fields are usually limited to single-moleculesettings, and their simulations are not robust enough. In this paper, wepresent PolyGET, a new framework for Polymer Forcefields with GeneralizableEquivariant Transformers. PolyGET is designed to capture complex quantuminteractions between atoms and generalize across various polymer families,using a deep learning model called Equivariant Transformers. We propose a newtraining paradigm that focuses exclusively on optimizing forces, which isdifferent from existing methods that jointly optimize forces and energy. Thissimple force-centric objective function avoids competing objectives betweenenergy and forces, thereby allowing for learning a unified forcefield ML modelover different polymer families. We evaluated PolyGET on a large-scale datasetof 24 distinct polymer types and demonstrated state-of-the-art performance inforce accuracy and robust MD simulations. Furthermore, PolyGET can simulatelarge polymers with high fidelity to the reference ab initio DFT method whilebeing able to generalize to unseen polymers.

PolyGET: Accelerating Polymer Simulations by Accurate and Generalizable Forcefield with Equivariant Transformer