Coupling High-Throughput Density Functional Theory, Automated Experimentation, and Adaptive Experimental Design To...

Developing new separation technologies for rare-earth elements is essential for sustaining the critical materials supply chain. Toward this end, we developed a pH-controlled solvent extraction strategy employing an aqueous-phase holdback agent to enable selective lanthanide separations, using an integrated computational, machine learning, and automated experimental high-throughput workflow. Database screening, density functional theory (DFT) calculations, and initial experimental evaluation identified oxaloacetic acid as a promising holdback agent that enhanced selective extraction of four lanthanides (Nd, Eu, Dy, Ho) when paired with the di(2-ethylhexyl)phosphoric acid (HDEHP or D2EHPA) extractant. Within this framework, the dependence of lanthanide solvent extraction was determined across a multidimensional chemical matrix (pH, extractant concentration, holdback agent concentration, and salt concentration) using automated, high-throughput experiments coupled with multi-objective Bayesian Optimization. Through efficient exploration of a large experimental space, we discovered Eu, Dy, and Ho could be selectively extracted over Nd in acidic media at pH ∼2.0, while a modest decrease in pH to ∼0.5 shifted the selectivity to enable Eu separation from Dy and Ho. The use of multi-objective Bayesian Optimization quickly yielded a 4-fold increase in separation factors compared to our HDEHP-only system, eliminating the need for a complete grid-based, exhaustive sampling approach. Overall, this work establishes a hierarchical and data-driven framework to identify selective separation conditions and provides a foundation for accelerating separations discovery and design.