Acquiring structural and mechanical information of a fibrous network through deep learning.

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Abstract

Fibrous networks play an essential role in the structure and properties of a variety of biological and engineered materials, such as cytoskeletons, protein filament-based hydrogels, and entangled or crosslinked polymer chains. Therefore, insight into the structural features of these fibrous networks and their constituent filaments is critical for discovering the structure-property-function relationships of these material systems. In this paper, a fibrous network-deep learning system (FN-DLS) is established to extract fibrous network structure information from atomic force microscopy images. FN-DLS accurately assesses the structural and mechanical characteristics of fibrous networks, such as contour length, number of nodes, persistence length, mesh size and fractal dimension. As an open-source system, FN-DLS is expected to serve a vast community of scientists working on very diverse disciplines and pave the way for new approaches on the study of biological and synthetic polymer and filament networks found in current applied and fundamental sciences.