Raw Data as the Verification Layer: Why AI-Driven Materials Discovery Needs Experimental Infrastructure

Artificial intelligence models for materials discovery are only as reliable as the data on which they are trained, yet systematic audits across the chemical sciences reveal quantifiable error rates in published experimental and computational data that often exceed the predictive accuracy of these models. Domain experts distinguish real from AI-generated characterization data at accuracy levels indistinguishable from random chance. This Viewpoint argues that the established culture of rigorous data collection, calibration, and metadata documentation that is standard at large experimental facilities represents a model for data integrity that the wider community must now adopt. Protecting and investing in these facilities, and in the expert staff who operate them, is not merely a matter of maintaining experimental capability; it is essential to ensuring that the data driving AI-enabled discovery can be trusted.