QRM: Hybrid Adversarial-Training Methods for 3D Virtual Microstructures

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This grant will support research that will develop a rigorous scientific method in quantifying the internal structure, or microstructure, of engineering materials, advancing the frontiers of science and benefiting the US economy. Microstructure determines the properties and performance of engineering materials, but is often described in qualitative rather than quantitative terms. This award supports fundamental research to build the scientific tools to quantify microstructure, and enable the creation of digital representations of microstructure that can be used for computational simulations to predict material performance in structural and functional applications. This project uses state-of-the-art data science and machine-learning (ML), to build quantitatively accurate, high-fidelity virtual microstructures, and combines these tools with physics-based computational models to predict how materials in service respond to extreme environments. The resulting capabilities allow for the design of high-performance materials for manufacturing, infrastructure, and aerospace, and have the potential to transform how materials are brought from the laboratory to commercial applications.This project uses state-of-the-art data science and machine-learning to build virtual microstructures with bounded uncertainty. The approach builds upon "shape-optimizers" existing within the DREAM.3D synthetic microstructure software, and the nascent capabilities of generative adversarial networks (GAN). These methods will be statistically coupled to experimental data for gains in quantitative representation accuracy. The virtual microstructures represented in phase-field simulations will serve as one quality metric for the ML prediction. The hybrid method explores and exploits use of the GAN methods to offset weaknesses within shape-optimizer and phase-transformation methods. The goal is to obtain complete, realistic, and accurate virtual structures. To demonstrate the gains from the new hybrid method, uncertainty quantification (UQ) techniques will be concurrently developed using both virtual "materials phantoms," and evaluations of material descriptors from both synthetic structures and experiments. The effort builds from open-source software and public datasets in the Materials Engineering and Machine Learning fields.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.