aiqbench analysis
The paper shows that a single machine-learned interatomic potential used as a stability filter missed most DFT-stable materials in a 25,000-material benchmark, and it proposes an audit pipeline combining adversarial testing, statistical bounds, and Lean 4 proofs. The results are benchmark-specific, and the 25% discovery gain comes from one thermoelectric case study, so the generalization claim needs independent replication. For ML and materials readers, the main takeaway is that architecture-specific blind spots can be nearly uncorrelated, so ensembles or per-model audits may matter more than a single model's aggregate accuracy.
Proof-Carrying Materials: Falsifiable Safety Certificates for Machine-Learned Interatomic Potentials
arXiv:2603.12183v3 Announce Type: replace-cross Abstract: 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 ESPRESSO validation (20/20 converged; median DFT/CHGNet force ratio 12x). A risk model trained on PCM-discovered features predicts failures on unseen materials (AUC-ROC = 0.938 +/- 0.004) and transfers across architectures (cross-MLIP AUC-ROC ~ 0.70; feature importance r = 0.877). In a thermoelectric screening case study, PCM-audited protocols discover 62 additional stable materials missed by single-MLIP screening - a 25% improvement in discovery yield.
Abstract: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 ESPRESSO validation (20/20 converged; median DFT/CHGNet force ratio 12x). A risk model trained on PCM-discovered features predicts failures on unseen materials (AUC-ROC = 0.938 +/- 0.004) and transfers across architectures (cross-MLIP AUC-ROC ~ 0.70; feature importance r = 0.877). In a thermoelectric screening case study, PCM-audited protocols discover 62 additional stable materials missed by single-MLIP screening - a 25% improvement in discovery yield.
Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
arXiv:2603.12183 [cond-mat.mtrl-sci]
(or arXiv:2603.12183v3 [cond-mat.mtrl-sci] for this version)
https://doi.org/10.48550/arXiv.2603.12183 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 253 words