ML models for materials science need formal safety audits—this work shows single models have severe blind spots, but systematic falsification and confidence bounds can identify reliable predictions and improve discovery by 25%.
Machine-learned models for predicting material properties often fail silently. This paper introduces Proof-Carrying Materials, a system that audits these models through adversarial testing, statistical confidence bounds, and formal verification to identify which predictions are trustworthy.