Microstructure modeling of deformed alloys using contrastive conditional generative adversarial networks

High entropy alloys (HEAs) tend to exhibit good mechanical properties, making them potential candidates for various applications. However, tailoring the alloys for target properties requires extensive exploration of microstructure configurations and corresponding properties either through experiments or numerical simulations. Leveraging recent advances in generative modeling, the deformation behavior of CoCrFeNiTa$_{0.395}$ alloy is modeled as conditional generation of deformed microstructures based on processing conditions. To achieve this, a Conditional Generative Adversarial Network (CGAN) model is developed, which synthesizes a deformed microstructure based on temperature and strain rate parameters. A contrastive conditional loss is utilized to induce similarity bias which effectively deals with data sparsity. To help the model learn intricate features across a wide range of process parameters, additional architectural mechanisms like self-attention are employed. Our evaluations reveal good qualitative and quantitative similarities between experimental and predicted microstructures. We also propose a modified contrastive loss for continuous conditioning variables and briefly discuss the ongoing work on demonstrating its generalization capability.