用机器学习加速聚合物熔体中多体色散力的计算，提升模拟效率。
Machine learning surrogate models of many-body dispersion interactions in polymer melts
- 基于精简版SchNet架构，只保留关键原子连接并用可训练基函数编码结构。
- 在聚乙烯、聚丙烯等熔体数据上预测准确，能捕捉色散力衰减规律。
- 计算高效，适合大规模分子模拟，尤其适合需要精确色散力的系统。
精确预测多体色散（MBD）相互作用对于理解复杂分子体系中的范德华力至关重要。然而，MBD计算的高成本限制了其在大规模模拟中的直接应用。本文提出一种专为聚合物熔体设计的机器学习代理模型，以预测其中的MBD力。该模型基于剪枝后的SchNet架构，仅保留最相关的原子连接，并引入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该模型，结果显示其具有高预测精度和强泛化能力，能够有效捕捉MBD相互作用的特征衰减行为，为截断策略优化提供依据。该模型计算效率高，可实际用于大规模分子模拟中引入MBD效应。
原文摘要 · Abstract (English)
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.
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