PolyGET: Accelerating Polymer Simulations by Accurate and Generalizable
  Forcefield with Equivariant Transformer
Rui Feng Huan Tran Aubrey Toland Binghong Chen Qi Zhu Rampi Ramprasad Chao Zhang
Polymer simulation with both accuracy and efficiency is a challenging task.
Machine learning (ML) forcefields have been developed to achieve both the
accuracy of ab initio methods and the efficiency of empirical force fields.
However, existing ML force fields are usually limited to single-molecule
settings, and their simulations are not robust enough. In this paper, we
present PolyGET, a new framework for Polymer Forcefields with Generalizable
Equivariant Transformers. PolyGET is designed to capture complex quantum
interactions between atoms and generalize across various polymer families,
using a deep learning model called Equivariant Transformers. We propose a new
training paradigm that focuses exclusively on optimizing forces, which is
different from existing methods that jointly optimize forces and energy. This
simple force-centric objective function avoids competing objectives between
energy and forces, thereby allowing for learning a unified forcefield ML model
over different polymer families. We evaluated PolyGET on a large-scale dataset
of 24 distinct polymer types and demonstrated state-of-the-art performance in
force accuracy and robust MD simulations. Furthermore, PolyGET can simulate
large polymers with high fidelity to the reference ab initio DFT method while
being able to generalize to unseen polymers.