相关实验视频
Updated: Feb 20, 2026

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Blast Quantification Using Hopkinson Pressure Bars
Published on: July 5, 2016
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平面-局部压力定义的量子方法用于机器学习潜力
1Brunel University of London Kingston Lane Uxbridge Middlesex, Uxbridge UB8 3PH, United Kingdom.
The Journal of chemical physics
|February 18, 2026
概括
平面方法 (MoP) 准确计算不均质流体中的应力,与病毒应力张量不同. 对机器学习潜力的MoP的这种扩展对于非平衡分子动力学模拟至关重要.
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.
- 统计力学就是统计力学.
背景情况:
- 在工程和分子建模中,压力至关重要.
- 病毒应力张量对于不均质的流体是不准确的,在流体动力学和非平衡分子动力学 (NEMD) 模拟中是必不可少的.
研究的目的:
- 将平面 (MoP) 应力计算方法扩展到MACE潜力,一个机器学习 (ML) 潜力.
- 在水-氧化接口和非平衡条件等场景中验证MACE潜力的MoP.
主要方法:
- 使用欧文和柯克伍德理论框架,为MACE潜力推导出局部应力.
- 将平面方法 (MoP) 应用于水-氧化接口模拟.
- 在非平衡模拟中,在受MoP限制的控制体积中证明了应力保存.
主要成果:
- 该MoP准确地测量了在水-氧化接口的力量平衡,在那里病毒应力张量器失败.
- 平面应力定义远离平衡是有效的,在每一个时间步骤中都显示了确切的保存.
- 这项研究将压力直接与保存方程联系起来,证明了在NEMD系统中的有效性.
结论:
- 扩展的MoP提供了一种有效和准确的方法来计算使用ML潜力的不均质流体中的应力.
- 这项工作对于在NEMD和分子流体动力学中应用ML的基础.
- 提供了开源代码来复制结果,并将MoP应用于MACE系统.
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