When May a Model Replace the Experiment? Audits, Licenses, and the Price of Trust in Surrogate-Driven Design
Design campaigns in chemistry, materials science, and machine learning share a bottleneck: determining how good a candidate truly is requires an expensive evaluation - an experiment, a first-principles simulation, or a full training run. Machine-learning surrogates that predict these outcomes are increasingly used not only to propose candidates but to grade them, and even to feed their own predictions back into the search as though they were measurements. Through mathematical analysis validated on three exhaustively ground-truthed design tasks, we establish when this practice is safe, what any certificate of safety must cost, and when the substitution provably pays. Predictive accuracy ca...
| Authors | Shuangxiu, Ma, Wenhe et al. |
|---|---|
| Primary category | cs.LG |
| Published | 2 August 2026 |
| Last updated at source | 24 August 2026 |
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