自主材料发现中的成本感知恢复路径识别与贝叶斯优化

中文摘要 AI 辅助

自主实验室实现了实验执行自动化，但还需决定优化哪条恢复路径。本文将此表述为一个顺序决策问题，包含离散的路径识别阶段和异构实验成本下的连续路径内优化阶段。所实现的协同学习方法，结合了受EC2启发的成本敏感贝叶斯假设判别策略与高斯过程贝叶斯优化。在明确假设下，一次固定预算活动尝试的预期花费受预期路径识别成本和路径内优化预算上限限制。通过合成基准测试评估该方法，其性能与神谕路径贝叶斯优化参考及强大的分裂板基线相当，在一个受NdFeB启发的实例中避免了先提交基线的模拟惩罚。还表征了结论对假设成本模型的敏感性，代码和基准开源。

英文摘要

Autonomous laboratories automate experimental execution, but a campaign must also decide which recovery pathway merits optimization. We formulate this as a sequential decision problem with a discrete pathway-identification stage and a continuous within-pathway optimization stage under heterogeneous experimental costs. Our implementation, Coactive learning, combines a cost-sensitive Bayesian hypothesis-discrimination policy motivated by EC2 (Golovin et al., 2010) with Gaussian-process Bayesian optimization (Srinivas et al., 2010). Under explicitly stated assumptions, the expected spend of one fixed-budget campaign attempt is bounded by the expected pathway-identification cost plus the capped within-pathway optimization budget. We evaluate the method on synthetic benchmarks constrained by selected results reported for PNNL's CICERO selective-precipitation study (Ritchhart et al., 2026). The method performs comparably to an oracle-pathway Bayesian-optimization reference and to a strong split-plate baseline that discriminates pathways with its first plate, without receiving an oracle label for the correct pathway. It is given a candidate hypothesis space and a diagnostic likelihood model. On an NdFeB-inspired instance, it avoids the simulated penalty of a commit-first baseline that initially selects a plausible but inferior hydroxide pathway. This hypothetical wrong-first-commitment scenario is motivated by the hydroxide-oxalate performance contrast reported by CICERO. We characterize the sensitivity of these conclusions to the assumed cost model. The code and benchmark are open source.

发表机构

• Coactive Science（协同科学）
• California Institute of Technology（加州理工学院）

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