Principal Investigator: Amanda Krause
Co-PI: Michael Tonks, Joel Harley
Sponsor: Department of Energy
Start Date: September 15, 2020
End Date: September 14, 2022
The goal of the proposed work is to combine deep, model-based reinforcement learning and transfer learning to elucidate one of the most fundamental, yet poorly understood, mechanisms in materials science: abnormal grain growth. We hypothesize that abnormal grain growth is the result of highly anisotropic grain boundary character networks, where a unique combination of neighboring grain boundaries incentivize accelerated growth. We will test this hypothesis with an anisotropic mesoscale grain growth model that incorporates the complexity of grain boundary character and energy anisotropy. Rather than rely on heuristics to define the grain growth behavior, we will teach the mesoscale grain growth model how to simulate grain growth. This is accomplished by integrating experimental microstructure data with model-based machine learning strategies. A machine learning approach is necessary for us to capture the high combinatorial, highly complex space of grain boundary character in a grain growth model. The developed anisotropic mesoscale grain growth model will be a new tool for exploring the microstructural features and processing parameters critical to inducing and sustaining or inhibiting abnormal grain growth.