Cost-Aware Recovery-Pathway Identification and Bayesian Optimization for Autonomous Materials Discovery
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 capp...
| Authors | Debajyoti Ray, Niranjan Srinivas |
|---|---|
| Primary category | cs.AI |
| Published | 26 July 2026 |
| Last updated at source | 26 July 2026 |
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