Ultimately, yes, more power means time aka money to pursue a target freely while messing with feature engineering. Brute force a la full DL stack is not there yet for two reasons: on one side, the space domain to search for novel materials is immense; on the other side, novel materials found through ML methods must be stable somewhere in their physics state diagram, synthesizable to be manufactured properly and cheap enough to be worth engineering deployment. The x10 process acceleration (from 20-30 years to 2-3 years) is actually in the space domain search thanks to ML methods working through several thousand experiments like in the linked article, not in the engineering readiness protocol for a candidate novel material from the lab confirmation to the real application. Outsiders can help as well by implementing their own pipeline after collecting their niche-specific datasets through journal papers, conference contributions and meeting minutes. I for example am interested in novel alloys or steels for Gen IV nuclear and now creating my own dataset for a first shot, having got a benchmark already from a known, valdated and successfully deployed material.
