物理一致的自我扩散系数计算与分子动力学和象征回归
Dimitrios Angelis1, Chrysostomos Georgakopoulos1, Filippos Sofos1
1Condensed Matter Physics Laboratory, Department of Physics, University of Thessaly, 35100 Lamia, Greece.
机器学习现在使用简单的宏观性质预测分子流体中的自我扩散系数. 这绕过了复杂的原子模拟,为散装和封闭系统提供了通用方法.
科学领域:
- 计算物理和化学 计算物理和化学
- 材料科学是一种材料科学.
- 化学工程是化学工程的组成部分.
背景情况:
- 计算自我扩散系数对于理解散装和封闭系统中的流体行为至关重要.
- 传统的方法,如平均平方位移,是计算密集和复杂的.
- 开发高效的流体动力学预测模型是一个持续的挑战.
研究的目的:
- 开发一种通用,计算效率高的方法来计算分子流体中的自我扩散系数.
- 导出分析表达式,将自我扩散系数与宏观流体特性相关联.
- 绕过传统的原子级仿真方法.
主要方法:
- 利用机器学习,特别是符号回归,在分子动力学模拟数据上训练.
- 与宏观参数相关的自我扩散系数:密度,温度和限制宽度.
- 使用遗传编程来选择简单,可解释的象征表达式.
主要成果:
- 在九种分子流体中获得了自我扩散系数的新分析表达式.
- 提取了适用于所有测试流体的通用方程,捕捉了分子行为.
- 证明了对自我扩散系数的准确预测,绕过了要求计算的方法.
结论:
- 机器学习为预测自我扩散系数提供了一种强大,高效的工具.
- 衍生出的通用方程为流体行为提供了一个物理上一致和可解释的模型.
- 这种方法推进了基本的理解,并有助于设计纳米尺寸的限制装置.
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