Accurate prediction of many-body dispersion (MBD) interactions is essential for understanding the van der Waals forces that govern the behavior of many complex molecular systems. However, the high computational cost of MBD calculations limits their direct application in large-scale simulations. In this work, we introduce a machine learning surrogate model specifically designed to predict MBD forces in polymer melts, a system that demands accurate MBD description and offers structural advantages for machine learning approaches. Our model is based on a trimmed SchNet architecture that selectively retains the most relevant atomic connections and incorporates trainable radial basis functions for geometric encoding. We validate our surrogate model on datasets from polyethylene, polypropylene, and polyvinyl chloride melts, demonstrating high predictive accuracy and robust generalization across diverse polymer systems. In addition, the model captures key physical features, such as the characteristic decay behavior of MBD interactions, providing valuable insights for optimizing cutoff strategies. Characterized by high computational efficiency, our surrogate model enables practical incorporation of MBD effects into large-scale molecular simulations.

翻译：准确预测多体色散相互作用对于理解范德华力至关重要，该力主导着众多复杂分子系统的行为。然而，MBD计算的高昂计算成本限制了其在大规模模拟中的直接应用。本研究提出了一种专门用于预测聚合物熔体中MBD力的机器学习代理模型，该系统既需要精确的MBD描述，又为机器学习方法提供了结构优势。我们的模型基于经过剪裁的SchNet架构，该架构选择性保留最相关的原子连接，并整合了可训练的径向基函数进行几何编码。我们在聚乙烯、聚丙烯和聚氯乙烯熔体的数据集上验证了代理模型，证明其在不同聚合物体系中具有高预测精度和稳健的泛化能力。此外，该模型捕捉了关键物理特征，例如MBD相互作用的特征衰减行为，为优化截断策略提供了重要参考。凭借其高计算效率，我们的代理模型使得将MBD效应实际纳入大规模分子模拟成为可能。