Multi-target digital material design via a conditional denoising diffusion probability model

Multi-target digital material design has been challenging due to the expansive design space and instability of traditional methods in satisfying multiple objectives. This work proposes and demonstrates a customizer based on a classifier-free, conditional denoising diffusion probability model (cDDPM) to efficiently create the layouts of digital materials meeting the design goal of multiple mechanical properties all together. A case study has been conducted based on a micro mechanical resonator with four pre-assigned resonant frequencies. Using 29,430 samples generated via finite element analysis (FEA), the cDDPM is trained to simultaneously customize up to four vibrational modes, achieving over 95% prediction accuracy. Furthermore, the cDDPM approach also shows superior performances in the single-target customization for up to 99% in prediction accuracy when compared with traditional conditional generative adversarial networks (cGANs). As such, the proposed design framework provides a highly customizable and robust methodology for the design of complicated digital materials.