The deep-learning-based evolutionary framework trained by high- throughput molecular dynamics simulations for composite microstructure design

SH Chen and N Xu, COMPOSITE STRUCTURES, 318, 117118 (2023).

DOI: 10.1016/j.compstruct.2023.117118

The interfacial shear behavior, including the elastic deformation stage, the debonding stage and the sliding stage, is of critical importance in determining the overall performance of a composite. Here a deep- learning-based framework that integrates the generative adversarial network (GAN) and the genetic algorithm (GA) is proposed to establish a bidirectional mapping between the interfacial microstructure and the interfacial shear behavior of SiC fiber reinforced SiC composite (SiCf/SiC). High-throughput molecular dynamics (MD) simulations are carried out to collect the strain-stress curves of the SiCf/SiC interfaces under shear tractions, considering the exhaustive variations in the interfacial microstructure and the temperature. The well-trained GAN model can predict the shear strain-stress curve of an arbitrarily given interfacial microstructure with excellent robustness and accuracy. In the reverse direction, the GA is utilized to infer the interfacial microstructure given desired shear properties. We demonstrate that the proposed GAN-GA protocol can enhance the physical modeling of conventional continuum-scale simulation and efficiently identify the microstructure leading to optimal mechanical properties. Such capability can greatly facilitate the demand-oriented development of composite materials, thereby contributing to intelligent manufacturing.

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