桥梁功能作为辐射分布函数的函数:操作员学习和应用程序
Martin Panholzer1, Michael Haring2, Thomas Wallek2
1Uni Software Plus GmbH, Linzer Strasse 6, 4320 Perg, Austria.
Physical review. E
|October 21, 2025
概括
机器学习预测了桥梁功能,改善了分子系统的积分方程理论. 这提高了对Lennard-Jones,Mie和硬球等流体的预测,推进了分子模拟.
科学领域:
- 计算物理学的计算物理.
- 统计力学就是统计力学.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 整方程理论,如奥恩斯坦-泽尼克方程,对于计算分子系统属性至关重要.
- 超联网链 (HNC) 近似,一个常见的关闭关系,忽略了桥梁功能,限制了它的准确性.
研究的目的:
- 开发一种机器学习方法来预测桥梁功能.
- 通过结合神经网络预测的桥梁函数来提高积分方程理论的准确性.
主要方法:
- 使用蒙特卡洛模拟伦纳德-斯流体的桥梁函数训练了一个深度操作员网络.
- 经过训练的网络根据辐射分布函数预测桥梁功能.
- 改进的HNC关闭,包括预测的桥梁功能,被代地解决.
主要成果:
- 与标准HNC相比,基于神经网络的桥梁功能显著改善了辐射分布函数和压力的预测.
- 该方法证明了普遍性,显示了Mie和硬球体流体的良好一致性,使用在Lennard-Jones数据上训练的模型.
结论:
- 机器学习提供了一个强大的工具,可以在分子模拟中增强积分方程理论.
- 拟议的方法为研究古典分子系统提供了更准确和更通用的方法.
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