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arXiv AI By Niranjan Srinivas, Debajyoti Ray, Elias Nakouzi

Decision-Focused Active Learning for Scale-Aware Critical-Materials Recovery

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The paper presents a decision‑focused active learning framework for optimizing scale‑aware recovery of critical materials, using data from the Pacific Northwest National Laboratory’s CICERO workflow on autonomous selective precipitation. By adaptively selecting experiments based on prior results, the method achieves the best recorded enrichment of recycled neodymium‑iron‑boron magnets with roughly half the number of experiments compared to non‑adaptive approaches, and demonstrates similar efficiency for samarium‑cobalt magnets. The authors propose a Bayesian risk‑based batch selection strategy and outline a prospective test plan with standardized logging and economic validation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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