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Updated: Jul 15, 2025

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
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数据驱动的方法,以粗粒简单的液体在限制的限制

Ishan Nadkarni1, Haiyi Wu2, Narayana R Aluru1,2

  • 1Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States.

Journal of chemical theory and computation
|October 4, 2023
PubMed
概括

我们使用深度神经网络 (DNN) 开发了一个数据驱动的框架,以确定局限空间中的液体的粗粒度 (CG) 列纳德-斯 (LJ) 潜在参数. 这种方法准确地预测了流体的行为,并增强了粗粒技术.

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