  Autonomous materials research labs require the ability to combine and learn
from diverse data streams. This is especially true for learning material
synthesis-process-structure-property relationships, key to accelerating
materials optimization and discovery as well as accelerating mechanistic
understanding. We present the Synthesis-process-structure-property relAtionship
coreGionalized lEarner (SAGE) algorithm. A fully Bayesian algorithm that uses
multimodal coregionalization to merge knowledge across data sources to learn
synthesis-process-structure-property relationships. SAGE outputs a
probabilistic posterior for the relationships including the most likely
relationships given the data.
