definitely: https://arxiv.org/abs/1709.02727 https://arxiv.org/abs/1709.02727 I work kind of adjacent to two of the supervisors on this project at NIST, and th

definitely: https://arxiv.org/abs/1709.02727 https://arxiv.org/abs/1709.02727 I work kind of adjacent to two of the supervisors on this project at NIST, and the workflow for these types of projects usually goes: (1) build predictive model of chemical composition -> superconductivity from past experiments or databases of simulations (2) Build and program automated sample prep (this is actually the hardest part, not the machine learning) (3) Build and program automated structural characterization and superconductivity measurement The difficulty is finding a system whose design space can be explored with and measured with automated tools, otherwise the machine learning isn't used effectively. As others have noted the models are decades old in some cases, what's driving this is researchers who know how to automate traditional mat sci workflows and know enough about machine learning to pick the automated runs optimally