Paper deep dive

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

Year: 2026Venue: arXiv preprintArea: cond-mat.mtrl-sciType: PreprintEmbeddings: 4

Intelligence

Status: succeeded | Model: Gemma-4-26B-A4B | Prompt: intel-v1 | Confidence: 90%

Last extracted: 7/12/2026, 2:06:46 AM

Summary

The paper introduces a multi-agentic workflow that leverages AI agents and automated instruments to recover critical materials from complex feedstocks like produced water and magnet leachates. By employing selective precipitation with simple chemicals, the approach significantly accelerates the development of scalable separation processes from months or years to just days.

Entities (8)

Agentic Workflow · workflow · 95%Critical materials · substance · 95%AI Agents · technology · 90%Automated instruments · technology · 90%Magnet leachates · feedstock · 90%Produced water · feedstock · 90%Selective precipitation · process · 90%Simple chemicals · reagent · 85%

Relation Signals (8)

Agentic Workflow → enables → Critical materials recovery

confidence 95% · enables the recovery of critical materials from produced water and magnet leachates

Agentic Workflow → processes → Produced water

confidence 90% · recover critical materials from produced water

Agentic Workflow → processes → Magnet leachates

confidence 90% · recover critical materials from produced water and magnet leachates

Agentic Workflow → uses → AI Agents

confidence 90% · deploys a series of AI agents and automated instruments

Agentic Workflow → uses → Automated instruments

confidence 90% · deploys a series of AI agents and automated instruments

Simple chemicals → areusedin → Selective precipitation

confidence 85% · using simple chemicals

Selective precipitation → ismethodof → Agentic Workflow

confidence 85% · via selective precipitation

Agentic Workflow → accelerates → Separation development timeline

confidence 80% · accelerating the development of efficient, adaptable, and scalable separations to a timeline of days, rather than months and years

Cypher Suggestions (0)

No Cypher suggestions yet.

Abstract

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.

PDF

Open source PDF →

PDF not stored locally. Use the link above to view on the source site.

Full Text

4,023 characters extracted from source content.

Expand or collapse full text

Skip to main content arXiv is now an independent nonprofit! Learn more × Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Condensed Matter > Materials Science arXiv:2603.15491v1 (cond-mat) [Submitted on 16 Mar 2026] Title: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 View a PDF of the paper titled Agentic workflow enables the recovery of critical materials from complex feedstocks via selective precipitation, by Andrew Ritchhart and 7 other authors View PDF 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. Subjects: Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI) Cite as: arXiv:2603.15491 [cond-mat.mtrl-sci] (or arXiv:2603.15491v1 [cond-mat.mtrl-sci] for this version) https://doi.org/10.48550/arXiv.2603.15491 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Maxim Ziatdinov [view email] [v1] Mon, 16 Mar 2026 16:17:26 UTC (2,385 KB) Full-text links: Access Paper: View a PDF of the paper titled Agentic workflow enables the recovery of critical materials from complex feedstocks via selective precipitation, by Andrew Ritchhart and 7 other authorsView PDF view license Current browse context: cond-mat.mtrl-sci < prev | next > new | recent | 2026-03 Change to browse by: cond-mat cs cs.AI References & Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) IArxiv recommender toggle IArxiv Recommender (What is IArxiv?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Instant research discovery