|About this Abstract
||Materials Science & Technology 2020
||Materials Design through AI Composition and Process Optimization
||Realistic 3D Microstructure Generation via Generative Adversarial Networks
||Elizabeth A. Holm, Tim Hsu, William K Epting, Hokon Kim, Harry W. Abernathy, Gregory A. Hackett, Anthony D. Rollett, Paul Salvador
|On-Site Speaker (Planned)
||Elizabeth A. Holm
Generating large data sets of realistic, statistically equivalent, 3D microstructures is a prerequisite for computational surveys and optimization approaches. Using large-scale 3D microstructural data, a Generative Adversarial Network (GAN) model has been implemented to learn and generate realistic 3D polyphase solid oxide fuel cell microstructures. Nearly limitless microstructural instances can be generated at relatively minimal cost compared to conventional simulation/statistical approaches. Besides being visually similar to the experimental microstructures, the GAN-generated microstructures are statistically similar with respect to geometric and topological metrics (e.g., particle size, surface area, triple-phase-boundary density). Furthermore, performance simulations applied to the GAN-synthetic microstructures result in realistic electrochemical response. This suggests the GAN model is capable of learning and generating microstructures that captures all salient aspects of the target system. Intriguingly, limitations of the GAN in resolving outlier structures not only provide materials insight, but also reveal opportunities to understand and improve the GAN approach itself.