SciAI for Grain Mapping with Electron Backscatter Diffraction: Leveraging Physics-Based Constraints and Uncertainty Propagation

Material science problems are often information rich, but sparsely sampled. Here we present how material science knowledge can be encoded into active learning frameworks to efficiently navigate such search spaces. The physics-based constraints limit the solution space to only relevant solutions. We present the example of electron backscatter diffraction tomography, where each location in 3D space has orientation described by a 4D set of quaternions. As data is acquired, the goal is to: 1. Discover the structure of the data set with some measure of cluster membership probability, 2. Predict the grain map in unmeasured locations, and 3. Choose the best measurement to take next. This method explicitly handles uncertainty at each step, demonstrating the importance of uncertainty propagation for appropriate confidence in the predictions. By building physical knowledge and uncertainty propagation into these AI’s, this method takes advantage of the richness of the data, allows for more accurate, physically sound predictions with less data, and facilitates interpretability.