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Meeting

2026 TMS Annual Meeting & Exhibition

Symposium

AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling

Presentation Title

A Computational Framework for the Intelligent Discovery of Selective Mineral Processing Reagents: Case Study of Phosphate Flotation

Author(s)

Amir Eskanlou, William Xu, Jef Caers

On-Site Speaker (Planned)

Amir Eskanlou

Abstract Scope

Critical minerals are essential for the clean energy transition, with demand expected to rise 500% by 2050. Froth flotation is the dominant mineral processing method but relies on trial-and-error reagent design, leading to inefficiencies and poor selectivity. Phosphate, a key material for fertilizers and LFP batteries, presents major beneficiation challenges. Due to current reagent limitations, reverse flotation is commonly used, increasing water and reagent use, phosphate loss, and environmental impact. For example, Florida’s phosphate operations have generated over two billion tons of clay waste containing 600 million tons of phosphate and rare earth elements. This study introduces an AI-accelerated, first-principles framework to discover selective reagents for direct phosphate flotation. We target subtle quantum mechanical differences in mineral surfaces. Preliminary results show that reagents with carboxylic and/or sulfonic groups exhibit strong affinity for phosphate, especially in the presence of dissolved Mg²⁺ and Al³⁺, which enhance adsorption by modifying surface functional groups.

Proceedings Inclusion?

Planned:

Keywords

Extraction and Processing, Recycling and Secondary Recovery, Modeling and Simulation

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