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Updated: Jul 20, 2025

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
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Δ-学习应用于粗粒度同质液体

Aditi Khot1, Brett M Savoie1

  • 1Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47906, USA.

The Journal of chemical physics
|August 1, 2023
PubMed
概括

德尔塔学习模型通过学习与基于物理的潜力的差异来增强粗粒度分子动力学 (CGMD),优于仅基于ML的模型. 然而,由于固有的粗粒度错误,两者都难以超越基本的对式模型.

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