package that industry can use immediately: (i) a ranked shortlist of candidate chemistries for selective phosphate flotation, (ii) a Pareto frontier that makes the recovery–gangue rejection tradeoff explicit (maximize fluorapatite affinity while minimizing dolomite affinity), and (iii) uncertainty estimates that direct the next computations or lab tests to where they add the most value - reducing wasted computational and experimental effort. In practice, this enables a shorter development cycle with fewer wet-lab trials, clearer criteria for selecting “best-next” candidates, and a direct pathway to operational impact: lower reagent and water consumption, reduced phosphate losses to tailings, and improved concentrate quality via stronger carbonate rejection. Because the workflow is modular (computations → surrogate model → frontier → target validation), it can potentially be scaled and transferred to other critical-mineral systems where selectivity limits processing of lower-grade, more complex ores, supporting solutions that can be implemented, scaled, and trusted. ACKNOWLEDGEMENTS This work was funded through the Mineral-X Industrial Affiliates program, which is supported by affiliate members KoBold Metals, Bidra VC, Ero Copper, Fleet Space Technologies, Ideon Technologies, and Xcalibur Smart Mapping. We thank them for their support. REFERENCES Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. Oxford University Press. Wills, B. A., & Finch, J. A. (2016). Froth flotation. Wills’ Mineral Processing Technology, 7. Zhang, P., Snow, R., Yu, Y., & Bogan, M. (2001). Recovery of phosphate from Florida phosphatic clays. Final Report, FIPR Publication, 02–096.
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