机器学习识别无序材料中的无标度特性
Machine learning identifies scale-free properties in disordered materials
AI总结：
本研究利用双向CNN在无序结构与波局域化间进行预测和逆向生成，发现所生成材料具有重尾与枢纽原子的无标度特性，显著提升抗缺陷鲁棒性。
摘要：
无序系统中巨大的设计自由度拓展了信号处理的参数空间，使独特的信号流动得以存在，并区别于规则系统中的信号流动。然而，这种高度的自由度阻碍了针对目标功能对无序系统进行确定性设计。在此，我们采用机器学习（ML）方法来预测和设计无序结构中的波-物质相互作用，从而识别波的无标度特性。为了抽象并映射波行为与无序结构的特征，我们开发了从无序到局域化以及从局域化到无序的卷积神经网络（CNN）。每个CNN都能够瞬时预测无序结构中的波局域化，并根据给定局域化瞬时生成无序结构。我们证明，CNN生成的无序结构具有伴随重尾和枢纽原子的无标度特性，其对材料或结构缺陷等意外缺陷的鲁棒性提升了多个数量级。我们的结果验证了ML网络结构在决定ML生成的实空间结构方面的关键作用，这些结构可用于设计抗缺陷且可高效调谐的器件。
英文摘要：
The vast amount of design freedom in disordered systems expands the parameter space for signal processing, allowing for unique signal flows that are distinguished from those in regular systems. However, this large degree of freedom has hindered the deterministic design of disordered systems for target functionalities. Here, we employ a machine learning (ML) approach for predicting and designing wave-matter interactions in disordered structures, thereby identifying scale-free properties for waves. To abstract and map the features of wave behaviours and disordered structures, we develop disorder-to-localization and localization-to-disorder convolutional neural networks (CNNs). Each CNN enables the instantaneous prediction of wave localization in disordered structures and the instantaneous generation of disordered structures from given localizations. We demonstrate that CNN-generated disordered structures have scale-free properties with heavy tails and hub atoms, which exhibit an increase of multiple orders of magnitude in robustness to accidental defects, such as material or structural imperfection. Our results verify the critical role of ML network structures in determining ML-generated real-space structures, which can be used in the design of defect-immune and efficiently tunable devices.
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