使用机器学习和非平衡分子动力学相结合,探测取决于速率的液体切割粘度
Hongyu Gao1, Minghe Zhu1, Jia Ma1,2
1Department of Materials Science & Engineering, Saarland University, Campus C6.3, 66123 Saarbrücken, Germany.
Journal of chemical theory and computation
|June 3, 2025
概括
这项研究将机器学习 (ML) 与非平衡分子动力学 (NEMD) 模拟相结合,以准确预测液体动态粘度. 综合方法克服了实验挑战,在各种切割速率中提供精确的粘度测量.
科学领域:
- 计算物理学的计算物理.
- 材料科学是一种材料科学.
- 类风病学 类风病学 类风病学
背景情况:
- 在高切割速率下测量液体动态粘度在实验上具有挑战性.
- 控制热效应和解决高剪速是关键限制.
- 了解剪切稀释行为对于复杂的流体动力学至关重要.
研究的目的:
- 开发一种可靠的方法,用于在切割速率上准确预测粘度.
- 将机器学习与不平衡分子动力学 (NEMD) 模拟集成.
- 研究剪切速率,压力和温度对粘度的相互作用.
主要方法:
- 开发了一个监督的人工神经网络 (ANN) 模型用于粘度预测.
- 使用LAMMPS.利用非平衡分子动力学 (NEMD) 模拟.
- 实现了"fix npt/sllod",用于模拟中精确的恒压控制.
主要成果:
- 该ANN模型准确地预测粘度作为切割速率,压力和温度的函数.
- 观察到明显的剪切薄化趋势和分子形态的非单调变化.
- 证明温度对粘度的影响在高剪切速率下降.
结论:
- 用ML增强的NEMD为粘度预测提供了一个高效和准确的框架.
- 这项研究提供了关于在剪切应力下分子行为的见解.
- 这种方法有助于未来研究复杂的流体动力学和材料设计.
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