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• W3088409862 abstract "Abstract The vast amount of design freedom in disordered systems expands the parameter space for signal processing. However, this large degree of freedom has hindered the deterministic design of disordered systems for target functionalities. Here, we employ a machine learning 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 behaviors and disordered structures, we develop disorder-to-localization and localization-to-disorder convolutional neural networks, each of which enables the instantaneous prediction of wave localization in disordered structures and the instantaneous generation of disordered structures from given localizations. We demonstrate that the structural properties of the network architectures lead to the identification of scale-free disordered structures having heavy-tailed distributions, thus achieving multiple orders of magnitude improvement in robustness to accidental defects. Our results verify the critical role of neural network structures in determining machine-learning-generated real-space structures and their defect immunity." @default.
• W3088409862 created "2020-10-01" @default.
• W3088409862 creator A5021048827 @default.
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• W3088409862 creator A5059983787 @default.
• W3088409862 date "2020-09-24" @default.
• W3088409862 modified "2023-10-14" @default.
• W3088409862 title "Machine learning identifies scale-free properties in disordered materials" @default.
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• W3088409862 doi "https://doi.org/10.1038/s41467-020-18653-9" @default.
• W3088409862 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7519134" @default.
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• W3088409862 hasPublicationYear "2020" @default.
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