Predicting rare earth extraction efficiency with organophosphorus ligands: A data-driven machine learning approach

Qihan Zhang, Haitao Wang, Ziyi Liu, Zhaomin Hao, Wuping Liao,
Predicting rare earth extraction efficiency with organophosphorus ligands: A data-driven machine learning approach,
Journal of Rare Earths,
2025,
,
ISSN 1002-0721,
https://doi.org/10.1016/j.jre.2025.09.016.
(https://www.sciencedirect.com/science/article/pii/S100207212500328X)
Abstract: Organophosphorus ligands are widely employed as extractants in industrial rare-earth (RE) separation; however, their design and optimization have largely been guided by empirical methodologies rather than systematic, rational approaches. In this work, we present a data-driven machine learning framework for predicting the distribution ratios (D) of RE elements in extraction processes using various organophosphorus ligands. To support this effort, a curated database comprising over 3500 experimental D measurements was established, encompassing 43 distinct ligands and 16 RE elements (excluding radioactive promethium) under varied extraction conditions. By integrating ligand descriptors, metal ion properties, and extraction parameters, we developed a convolutional neural network (CNN) model that achieves robust performance, with R2 values of approximately 0.98 for training and 0.83 for testing. Our analysis further identified key factors – such as aqueous pH, ligand structural fragments, partial charges, metal ionic radii and topological features – that govern extraction behavior and correlate with specific mechanisms. Finally, the predictive capability of model was validated by accurately forecasting the D value of a newly synthesized ligand HA.
Keywords: Rare earths; Extraction; Machine learning; Organophosphorus ligands; Distribution ratios