机器学习辅助的聚乙烯多目标粗粒度策略
Jiaxian Zhang1,2, Hongxia Guo1,2
1Beijing National Laboratory For Molecular Sciences, Joint Laboratory of Polymer Sciences and Materials, State Key Laboratory of Polymer Physics and Chemistry, Institute of Chemistry Chinese Academy of Sciences, Beijing, China.
Macromolecular rapid communications
|October 1, 2025
概括
我们开发了一种机器学习策略,为聚合物创建精确的粗粒度 (CG) 模型. 这种方法确保了结构,热力学和动态的一致性,改进了聚合物模拟.
科学领域:
- 计算化学是一种计算化学.
- 材料科学是一种材料科学.
- 聚合物物理 聚合物物理
背景情况:
- 粗粒度 (CG) 分子动力学对于弥合原子模拟和宏观实验至关重要.
- 开发具有同时结构,热力学和动态一致性的CG模型是一个重大挑战.
研究的目的:
- 介绍一种机器学习辅助的多目标参数化策略,用于对抗性聚乙烯 (PS).
- 通过在优化中包含扩散系数来构建一个动态一致的CG模型.
主要方法:
- 使用了对聚钢的 2:1 映射方案.
- 集成支向量回归 (SVR) 和粒子集群优化 (PSO) 以优化莱纳德-斯参数.
- 优化参数以复制原子级辐射分布函数,密度,凝聚性能量密度和自我扩散系数.
主要成果:
- 在多个可观测的CG力场和全原子 (AA) 模拟之间取得了显著的一致性.
- 成功复制了聚烯的结构,热力学和动态特性.
- 证明了将扩散系数用于动态一致性的有效性.
结论:
- 开发的战略为预测性聚合物建模建立了一个强大的框架.
- 这种方法可以扩展到材料发现和合理的聚合物设计.
- 机器学习辅助的方法提高了粗粒度模型的准确性和可靠性.
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