Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery

evidence

Examining a random sample of the roughly 380,000 structures GNoME proposed as stable, the authors report finding none that met a three-part test of being credible, useful and novel; that many entries were compositional modifications of already known compounds; and that the outputs should be described as predicted crystalline inorganic compounds rather than by the more generic label "material". V55 verification basis: the article was not fetched — pubs.acs.org is blocked to this session — but search results carried the title, the DOI 10.1021/acs.chemmater.4c00643, the authors and their institution, and the substance of the three-part test and the conclusion, attributing them to this perspective. The size and selection method of their random sample were NOT retrieved and are not asserted.

A evidence is not a place. Drawing it on a map would assert something about the world that no stored fact supports.