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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.15491 |
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| _version_ | 1866917347595386880 |
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| author | Ritchhart, Andrew Allec, Sarah I. Butreddy, Pravalika Kulesa, Krista Wang, Qingpu Nguyen, Dan Thien Ziatdinov, Maxim Nakouzi, Elias |
| author_facet | Ritchhart, Andrew Allec, Sarah I. Butreddy, Pravalika Kulesa, Krista Wang, Qingpu Nguyen, Dan Thien Ziatdinov, Maxim Nakouzi, Elias |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_15491 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Agentic workflow enables the recovery of critical materials from complex feedstocks via selective precipitation Ritchhart, Andrew Allec, Sarah I. Butreddy, Pravalika Kulesa, Krista Wang, Qingpu Nguyen, Dan Thien Ziatdinov, Maxim Nakouzi, Elias Materials Science Artificial Intelligence 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. |
| title | Agentic workflow enables the recovery of critical materials from complex feedstocks via selective precipitation |
| topic | Materials Science Artificial Intelligence |
| url | https://arxiv.org/abs/2603.15491 |