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.

翻译：兼具精度与效率的聚合物模拟是一项具有挑战性的任务。机器学习力场已被开发用于实现从头算方法的精确性与经验力场的高效性。然而，现有机器学习力场通常局限于单分子场景，其模拟鲁棒性不足。本文提出PolyGET——一种基于等变Transformer的通用聚合物力场新框架。PolyGET通过名为等变Transformer的深度学习模型，能够捕获原子间复杂的量子相互作用，并泛化至各类聚合物族系。我们提出一种专注于力优化的全新训练范式，这与现有方法联合优化力与能量的方式不同。这种简化的以力为中心的目标函数避免了能量与力之间的目标竞争，从而能够学习跨不同聚合物族系的统一力场机器学习模型。我们在包含24种不同聚合物类型的大规模数据集上评估了PolyGET，其在力精度与鲁棒的分子动力学模拟中展现出最先进性能。此外，PolyGET能以高保真度模拟大型聚合物至参考从头算DFT方法，同时具备对未见聚合物的泛化能力。