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The distribution of material phases is crucial to determine the composite's mechanical property. While the full structure-mechanics relationship of highly ordered material distributions can be studied with finite number of cases, this relationship is difficult to be revealed for complex irregular distributions, preventing design of such material structures to meet certain mechanical requirements. The noticeable developments of artificial intelligence (AI) algorithms in material design enables to detect the hidden structure-mechanics correlations which is essential for designing composite of complex structures. It is intriguing how these tools can assist composite design. Here, we focus on the rapid generation of bicontinuous composite structures together with the stress distribution in loading. We find that generative AI, enabled through fine-tuned Low Rank Adaptation models, can be trained with a few inputs to generate both synthetic composite structures and the corresponding von Mises stress distribution. The results show that this technique is convenient in generating massive composites designs with useful mechanical information that dictate stiffness, fracture and robustness of the material with one model, and such has to be done by several different experimental or simulation tests. This research offers valuable insights for the improvement of composite design with the goal of expanding the design space and automatic screening of composite designs for improved mechanical functions. 

Collagen, a vital protein that provides strength to various body tissues, has a triple helix structure containing three polypeptide chains. The chains are composed mostly of a tripeptide of glycine (G), proline (P), and hydroxyproline (O). Using molecular dynamics simulations and theoretical analysis, the study examines the mechanical response of collagen triple helix structures, made up of three different tripeptide units, when subjected to different fracture loading modes. The results show that collagen with GPO tripeptide units at their C-terminal are mechanically stronger than the POG and OGP units with a single amino-acid frame shift. Our work shows that the N-terminal has less effect on collagen fracture than the C-terminal. The differences in mechanical response are explained by the heterogenous rigidity of the amino acid backbone and the resulting shear lag effect near the terminal. The findings have potential applications in developing tough synthetic collagen for building materials and may stimulate further studies on the connection between terminal repeats and the mechanical-thermal behavior of other structural proteins such as silk, elastin, fibrin, and keratin.