Two-dimensional (2D) covalent organic frameworks (COFs) are a promising class of materials exhibiting significant potential for diverse applications owing to their unique structural characteristics. In particular, some applications crucially rely on mechanoresponsive behaviors of 2D COFs, which thus require a comprehensive understanding. Herein, a machine-learning potential is developed for two representative monolayer 2D COFs (COF-1 and COF-5), which demonstrates both high computational efficiency and accuracy in molecular dynamics (MD) simulations of their mechanical behaviors. Our machine-learning MD simulations reveal distinct planar-to-nonplanar conformation transitions occurring in COF-1 and COF-5 at large tensile strain in the armchair direction. According to density functional theory calculations, the tension-induced conformation transitions in 2D COFs are driven by strong H-H steric repulsions between aryl rings within their deformed linker or knot components. The observed conformation transition is anticipated to provide a new strategy for modulating electronic properties, fracture behaviors, and gas adsorption characteristics of 2D COFs.

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• Clusters
• Organic compounds
• Polymers
• Machine learning
• Molecular dynamics