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arXiv · 2608.27873

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

Chuhan Yang·Chenxi Wang·Linhan Wu·Yuyang Liu
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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.

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Chuhan Yang, Chenxi Wang, Linhan Wu, Yuyang Liu. 2026-08-28. Anchored Scenario Coverage for Failure-Aware First-Hit Batch Inverse Design. https://arxiv.org/abs/2608.27873

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