Polyisoprene (PI) is a widely used polymer and constructing a systematic coarse-grained (CG) PI model with the structural and thermodynamic consistency with the underlying atomic model over a wide range of thermodynamic conditions is very important for the predictive capability of CG model on overall properties of PI polymer materials and the establishment of their structure-property relationship. However, as the number of tunable CG potential parameters and target properties grows, traditional parameter tuning methods become impractical. In this work, we present a novel approach for determining the optimal CGPI non-bonded potential parameters by employing Particle Swarm Optimization as the calibrator with machine learning-based models trained using molecular dynamics data. The resulting CG model is further augmented with temperature factors through a multistate parameterization approach. This enhancement ensures the model's temperature transferability of structure and thermodynamics in a wide temperature of 150K∼750K.

中文翻译：

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在机器学习的辅助下构建具有结构和热力学一致性的顺式1,4-聚异戊二烯的温度可传递粗粒度模型


聚异戊二烯（PI）是一种广泛使用的聚合物，构建系统的粗粒度（CG）PI模型，使其在广泛的热力学条件下与底层原子模型保持结构和热力学一致性，对于CG模型的预测能力非常重要。 PI高分子材料的综合性能及其构效关系的建立。然而，随着可调节 CG 潜在参数和目标属性数量的增加，传统的参数调节方法变得不切实际。在这项工作中，我们提出了一种新颖的方法，通过采用粒子群优化作为校准器，并使用分子动力学数据训练的基于机器学习的模型来确定最佳 CGPI 非键势参数。通过多状态参数化方法，生成的 CG 模型进一步增加了温度因素。这一增强保证了模型在150K∼750K宽温度范围内结构和热力学的温度传递能力。