🤖 AI Summary
This work addresses the challenge of selecting and optimizing recovery pathways under heterogeneous experimental cost constraints in autonomous materials discovery. The authors propose a two-stage sequential decision framework: first, a cost-aware active hypothesis discrimination method—based on EC2—is employed to identify high-potential pathways; subsequently, Gaussian process Bayesian optimization refines performance within the selected pathways. This approach uniquely integrates cost-sensitive hypothesis testing with Bayesian optimization, enabling efficient exploration without requiring prior labels of correct pathways and providing theoretical bounds on expected cost. Experiments on a CICERO-inspired synthetic benchmark demonstrate that the method matches the performance of an oracle optimizer, significantly outperforms split-plate baselines, and effectively avoids performance degradation caused by erroneously selecting hydroxide pathways.
📝 Abstract
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.