Proof-Carrying Materials: Falsifiable Safety Certificates for Machine-Learned Interatomic Potentials
Machine-learned interatomic potentials (MLIPs) are deployed for high-throughput materials screening without formal reliability guarantees. We show that a single MLIP used as a stability filter misses 93% of density functional theory (DFT)-stable materials (recall 0.07) on a 25,000-material benchmark. Proof-Carrying Materials (PCM) closes this gap through three stages: adversarial falsification across compositional space, bootstrap envelope refinement with 95% confidence intervals, and Lean 4 formal certification. Auditing CHGNet, TensorNet and MACE reveals architecture-specific blind spots with near-zero pairwise error correlations (r <= 0.13; n = 5,000), confirmed by independent Quantum ...
| Authors | Abhinaba Basu, Pavan Chakraborty |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 12 March 2026 |
| Last updated at source | 13 March 2026 |
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