Structural analysis of lanthanide–DOTAM coordination complexes and use of machine learning to rationally design materials with molecular recognition for selective rare earth recovery

Abstract

Rare earth elements (REEs) pose challenges for chemical separation due to similarities in charge and ionic radii. Developing materials with high metal specificity, such as molecularly imprinted polymers, could enhance selectivity, but there are limited details available to support the rational design of selective REE binding motifs. In the current study, structural characterization of REE–DOTAM (1,4,7,10-tetrakis(carbamoylmethyl)-1,4,7,10-tetraazacyclododecane) complexes was combined with density functional theory (DFT) and machine learning (ML) to provide specific structural descriptors that can be used to develop selective REE sites. Nineteen REE–DOTAM complexes were synthesized from mixed solvent systems (HO/DMF, HO/DMSO, and HO/DMA) and characterized using single-crystal X-ray diffraction. The resulting structural data were used to construct perfect templates (fixed ligand geometries without further relaxation) and relaxed templates (fully geometry-optimized structures). DFT-derived substitution energies revealed that selectivity between heavy and light REEs could be achieved, with larger energetic penalties observed for the perfect templates. Machine learning analysis attributed selectivity primarily to hydration energies in the perfect templates, with twist angles as the dominant structural control in the relaxed systems. Additional analysis of the ML output suggested specific conditions that could be targeted for rationally designed DOTAM-based materials for improved REE selectivity. Structural characterization of 19 rare earth element DOTAM complexes, Density Functional Theory (DFT) and Machine Learning (ML) methods were used to identify chemical descriptors for the rationally design of materials with molecular recognition. toc