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.

翻译：机器学习原子间势能（MLIPs）目前在高通量材料筛选中被广泛应用，但缺乏形式化的可靠性保证。我们通过25,000种材料的基准测试表明，单一MLIP作为稳定性过滤器会漏掉93%的密度泛函理论（DFT）稳定材料（召回率0.07）。可证伪材料（PCM）方法通过三个阶段填补这一空白：在成分空间中进行对抗性证伪、采用95%置信区间的自举包络细化，以及基于Lean 4的形式化验证。对CHGNet、TensorNet和MACE的审计揭示了架构特异性盲点，其成对误差相关性近乎为零（r ≤ 0.13；n = 5,000），该结果经独立Quantum ESPRESSO验证确认（20/20收敛；DFT/CHGNet力比中位数达12倍）。基于PCM发现特征训练的风险模型能有效预测未知材料的失效情况（AUC-ROC = 0.938 ± 0.004），并具备跨架构迁移能力（跨MLIP的AUC-ROC约0.70；特征重要性r = 0.877）。在热电材料筛选案例研究中，经PCM审计的筛选方案额外发现了62种被单一MLIP筛选遗漏的稳定材料——发现效率提升达25%。