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Updated: Feb 18, 2026

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无序固体中可塑性的普遍特征的结构-属性关系
E D Cubuk1, R J S Ivancic2, S S Schoenholz1,2
1Google Brain, Mountain View, CA 94043, USA.
概括
无序的固体通过粒子重新排列呈现塑性流动. 机器学习的"软度"指标预测了这些事件,揭示了从原子到颗粒的材料的普遍缩放规律.
科学领域:
- 材料科学
- 物理
- 计算科学
背景情况:
- 晶体固体通过粒子在缺陷周围的重新排列而塑性变形.
- 无序的固体也表现出塑性流动,但底层结构驱动因素的理解较少.
- 了解无序材料的可塑性对于预测它们的机械行为至关重要.
研究的目的:
- 在无序固体中将微观结构与宏观可塑性联系起来.
- 为了确定粒子重排的预测结构量.
- 在不同的物质尺度上研究这些关系的普遍性.
主要方法:
- 使用机器学习来设计称为"软度"的微观结构量.
- 进行实验和计算以测量软度和可塑性.
- 分析了软度,重新排列大小和产量应变的空间相关性和应变反应.
主要成果:
- 软度被确定为无序固体中的粒子重排的一个关键预测因素.
- 实验和计算结果显示了软度和可塑性的常见缩放规律.
- 这些普遍的共同点在广泛的材料尺寸和弹性模块中被观察到.
结论:
- 软度成功地将结构与无序固体中的可塑性联系在一起.
- 这项研究揭示了结构性质和塑性变形之间的普遍关系.
- 这些发现为了解各种无序材料的流动提供了一个统一的框架.
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