# Cost-Aware Recovery-Pathway Identification and Bayesian Optimization for Autonomous Materials Discovery

arXiv: https://arxiv.org/abs/2607.23896 · HTML: https://mlsanity.com/paper/2607.23896/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 41 references that are in the map.
- First posted on arXiv 26 Jul 2026.

## Builds on (most cited first, 16 of 16)

- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [A Tutorial on Thompson Sampling](https://mlsanity.com/paper/1707.02038/) (2017)
- [On Kernelized Multi-armed Bandits](https://mlsanity.com/paper/1704.00445/) (2017)
- [Unexpected Improvements to Expected Improvement for Bayesian Optimization](https://mlsanity.com/paper/2310.20708/) (2023)
- [Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement](https://mlsanity.com/paper/2105.08195/) (2021)
- [Batch Bayesian Optimization via Local Penalization](https://mlsanity.com/paper/1505.08052/) (2015)
- [Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration](https://mlsanity.com/paper/1304.5350/) (2013)
- [Adaptivity in Adaptive Submodularity](https://mlsanity.com/paper/1911.03620/) (2019)
- [Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs](https://mlsanity.com/paper/2111.06537/) (2021)
- [Active Learning and Bayesian Optimization: a Unified Perspective to Learn with a Goal](https://mlsanity.com/paper/2303.01560/) (2023)
- [Near-optimal Bayesian Active Learning with Correlated and Noisy Tests](https://mlsanity.com/paper/1605.07334/) (2016)
- [Minimum Cost Adaptive Submodular Cover](https://mlsanity.com/paper/2208.08351/) (2022)
- [A Tight Bound for Stochastic Submodular Cover](https://mlsanity.com/paper/2102.01149/) (2021)
- [Gaussian Process Bandit Optimization with Machine Learning Predictions and Application to Hypothesis Generation](https://mlsanity.com/paper/2601.22315/) (2026)
- [Agentic workflow enables the recovery of critical materials from complex feedstocks via selective precipitation](https://mlsanity.com/paper/2603.15491/) (2026)
- [Lower Bound on the Greedy Approximation Ratio for Adaptive Submodular Cover](https://mlsanity.com/paper/2405.14995/) (2024)

## Built on by (most cited first, 1 of 1)

- [Decision-Focused Active Learning for Scale-Aware Critical-Materials Recovery](https://mlsanity.com/paper/2609.09413/) (2026)

## Abstract (arXiv)

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.