mlsanityData to 14 Oct 2026

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

arXiv 2603.15491 ↗First posted 16 Mar 2026cond-mat.mtrl-sci cs.AIcited by 2

Not on the map. Why?

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.

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

1 of 23 references.

References · 1

  1. 2024The Faiss librarycited by 376

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025"DIVE" into Hydrogen Storage Materials Discovery with AI Agents
  2. 2026MatClaw: An Autonomous Code-First LLM Agent for End-to-End Materials Exploration
  3. 2026Towards Agentic Intelligence for Materials Science
  4. 2025Toward Greater Autonomy in Materials Discovery Agents: Unifying Planning, Physics, and Scientists
  5. 2025Operationalizing Serendipity: Multi-Agent AI Workflows for Enhanced Materials Characterization with Theory-in-the-Loop
  6. 2026Hierarchical Multi-Agent Reasoning Systems for Knowledge-Grounded Material Substitution
  7. 2025Towards Fully Automated Molecular Simulations: Multi-Agent Framework for Simulation Setup and Force Field Extraction
  8. 2025CIRO7.2: A Material Network with Circularity of -7.2 and Reinforcement-Learning-Controlled Robotic Disassembler ↗