As a researcher in this field I'll just add that in many cases, automating the mat sci workflows (the sample prep and the characterization) is a massive leap in and of itself, even without adding machine learning. The benefit of machine learning in many of these projects is to pick the automated runs optimally (choose he right neighborhood of composition space), which probably adds a 10-100x speedup on top of the already 100-1000x speedup gained from just not making and characterizing samples manually. It's truly a synergistic combination of advancements in both fields and has great potential for accelerated discovery. /shill
