提升复杂材料设计中首个有效方案的发现效率
Anchored Scenario Coverage for Failure-Aware First-Hit Batch Inverse Design
- 以高潜力候选为锚点，优化批量推荐的多样性与覆盖性
- 在超导与材料数据库上首次命中率显著提升，统计显著
- 适合高失败风险、预算有限的实验闭环设计场景
在易出错的闭环逆向设计中，尽早发现满足目标要求的有效设计方案是核心目标。传统批量基线方法按乘积形式的边际有效命中得分排序候选，但独立选取最高分项会因预测不确定性导致推荐冗余，浪费实验资源。本文提出ARC-SC（锚定风险约束场景覆盖），保留强边际候选作为锚点，并在风险支持约束下，通过最大化对预测目标场景的互补覆盖来分配剩余批次位置。在超导与JARVIS材料性能基准的冻结预言机闭环仿真中，ARC-SC实现了首次命中发现的统计显著提升，在更具挑战性的设计空间中也保持了方向有利的首次命中表现。结果表明，ARC-SC是一种基于POF锚点、场景感知的批量策略，能有效提升结构化实验失败条件下的早期有效目标发现能力。
原文摘要 · Abstract (English)
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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