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

arXiv: https://arxiv.org/abs/2603.15491 · HTML: https://mlsanity.com/paper/2603.15491/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 23 references that are in the map.
- First posted on arXiv 16 Mar 2026.

## Builds on (most cited first, 1 of 1)

- [The Faiss library](https://mlsanity.com/paper/2401.08281/) (2024)

## Built on by (most cited first, 2 of 2)

- [Cost-Aware Recovery-Pathway Identification and Bayesian Optimization for Autonomous Materials Discovery](https://mlsanity.com/paper/2607.23896/) (2026)
- [Decision-Focused Active Learning for Scale-Aware Critical-Materials Recovery](https://mlsanity.com/paper/2609.09413/) (2026)

## Abstract (arXiv)

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.