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网络与层级:无定形材料如何学会记忆

Networks and Hierarchies: How Amorphous Materials Learn to Remember.

机构信息

Institut für angewandte Mathematik, Universität Bonn, Endenicher Allee 60, 53115 Bonn, Germany.

Jawaharlal Nehru Centre for Advanced Scientific Research, Jakkar Campus, 560064 Bengaluru, India.

出版信息

Phys Rev Lett. 2019 Oct 25;123(17):178002. doi: 10.1103/PhysRevLett.123.178002.

DOI:10.1103/PhysRevLett.123.178002
PMID:31702267
Abstract

We consider the slow and athermal deformations of amorphous solids and show how the ensuing sequence of discrete plastic rearrangements can be mapped onto a directed network. The network topology reveals a set of highly connected regions joined by occasional one-way transitions. The highly connected regions include hierarchically organized hysteresis cycles and subcycles. At small to moderate strains this organization leads to near-perfect return point memory. The transitions in the network can be traced back to localized particle rearrangements (soft spots) that interact via Eshelby-type deformation fields. By linking topology to dynamics, the network representations provide new insight into the mechanisms that lead to reversible and irreversible behavior in amorphous solids.

摘要

我们研究了非晶态固体的缓慢和非热变形,并展示了随之而来的一系列离散塑性重排如何映射到有向网络上。该网络拓扑揭示了一组由偶尔的单向跃迁连接的高度连接区域。这些高度连接的区域包括层次化组织的滞后循环和子循环。在较小到中等应变下,这种组织导致近乎完美的回复点记忆。网络中的转变可以追溯到局部粒子重排(软点),它们通过埃舍尔型变形场相互作用。通过将拓扑结构与动力学联系起来,网络表示法为理解导致非晶固体可逆和不可逆行为的机制提供了新的见解。

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