Anchored Scenario Coverage for Failure-Aware First-Hit Batch Inverse Design

Abstract:Early discovery of at least one valid design satisfying a target requirement is a central objective in failure-prone closed-loop inverse design. A natural batch baseline ranks candidates by a product-form marginal valid-hit score, but selecting the highest-ranked candidates independently can produce redundant recommendations under predictive uncertainty and waste the experiment budget. We introduce ARC-SC(Anchored Risk-Constrained Scenario Coverage), a batch acquisition method that preserves strong marginal candidates as anchors and allocates the remaining batch positions by maximizing complementary coverage over predictive target scenarios under a risk-support constraint. In frozen-oracle closed-loop simulations on superconductivity and JARVIS materials-property benchmarks, ARC-SC yields a statistically supported improvement in first-hit discovery and remains competitive with directionally favorable first-hit performance on more challenging design space. These results establish ARC-SC as a POF-anchored, scenario-aware batch strategy for improving early valid-target discovery under structured experimental failure.

Optimization and Control (math.OC); Machine Learning (cs.LG)

arXiv:2608.27873 [math.OC]

(or arXiv:2608.27873v1 [math.OC] for this version)

https://doi.org/10.48550/arXiv.2608.27873 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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