The relationship between material processing, structure, and properties is challenging to understand and even harder to predict because it is non-linear, high-dimensional, and results from physical phenomena at many scales. While traditional materials design has relied on human intuition to interpret patterns in known materials and infer new ones with similar (hopefully improved) properties, emerging data science tools offer new strategies to expedite materials design. My group is working to gather these strategies into a common framework which can be applied across many different materials science problems. In this talk, I will share some vignettes illustrating our initial progress and discuss the challenges ahead.
