Agentic workflow enables the recovery of critical materials from complex feedstocks via selective precipitation

Andrew Ritchhart; Sarah I. Allec; Pravalika Butreddy; Krista Kulesa; Qingpu Wang; Dan Thien Nguyen; Maxim Ziatdinov; Elias Nakouzi

cond-mat.mtrl-sciMar 16, 2026

Agentic workflow enables the recovery of critical materials from complex feedstocks via selective precipitation

Authors: Andrew Ritchhart, Sarah I. Allec, Pravalika Butreddy, Krista Kulesa, Qingpu Wang, Dan Thien Nguyen, Maxim Ziatdinov, Elias Nakouzi

Organizations: Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99354, United States. · Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Seattle, Washington 98109, United States. · Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States.

Abstract

We present a multi-agentic workflow for critical materials recovery that deploys a series of AI agents and automated instruments to recover critical materials from produced water and magnet leachates. This approach achieves selective precipitation from real-world feedstocks using simple chemicals, accelerating the development of efficient, adaptable, and scalable separations to a timeline of days, rather than months and years.

Materials Science

Materials science studies how composition, structure, processing, and defects determine material properties and how to design useful materials.

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

Niranjan Srinivas, Debajyoti Ray, Elias Nakouzi

Coactive Inc. · Pacific Northwest National Laboratory

Active Learning Materials Science Adaptive Sampling

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Choosing a recovery process for scale-up requires connecting laboratory results with product requirements, process costs, and scale effects. We analyze records from Pacific Northwest National Laboratory's Computer Intelligence for Critical Element Recovery and Optimization (CICERO) workflow for autonomous selective precipitation. Active learning uses prior results to choose experiments. In a conditional retrospective benchmark with fitted models and recycled neodymium-iron-boron (NdFeB) magnet records, active learning finds the best recorded result with fewer experiments than nonadaptive space filling. Enrichment is the selected rare-earth-to-iron ratio relative to that in the feed. Adaptive policies reach the recorded enrichment maximum by 16 to 24 wells (individual experiments), versus 48. Our two-stage reconstruction ties two adaptive alternatives at 16 wells. Conditional analyses of recycled samarium-cobalt (SmCo) magnets show a Round 2 tradeoff between purity and nominal yield, the recovery fraction calculated from an assumed starting amount - NdFeB Round 1 routes differ in enrichment. Rankings for produced water from oil and gas extraction depend on phase and dilution assumptions requiring confirmation. We propose choosing batches by their expected reduction in downstream Bayes risk: the minimum expected loss among available process decisions under current beliefs. In exploratory simulations, a hybrid that filters candidates has lower estimated loss than the implemented joint search across routes and conditions. Differences involving the synthetic two-stage policy are small relative to estimation uncertainty. We outline a pre-registered prospective test under a shared loss and logging standard, requiring clarified measurements and records, a defined process decision and relevant outputs, credible economic inputs, and validation at the intended scale.

Abstract

Materials Science

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