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arXiv Machine Learning By Abhinaba Basu, Pavan Chakraborty

Proof-Carrying Materials: Falsifiable Safety Certificates for Machine-Learned Interatomic Potentials

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The paper introduces Proof‑Carrying Materials (PCM), a three‑stage framework that improves the reliability of machine‑learned interatomic potentials (MLIPs) used for high‑throughput materials screening. PCM combines adversarial falsification, bootstrap envelope refinement, and Lean 4 formal certification to close the large gap between MLIP predictions and density functional theory (DFT) stability, achieving a 93% reduction in missed stable materials on a 25,000‑material benchmark. The method uncovers architecture‑specific blind spots, provides a risk model with high predictive performance, and boosts discovery yield by 25% in a thermoelectric screening case study.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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