On the Importance\nof Configuration Search to the Predictivity\nof Lanthanide Selectivity

The lanthanide elements are crucial components in numerous\ntechnologies,\nyet their industrial production through liquid–liquid extraction\ncontinues to be economically and environmentally costly due to the\nchallenge of separating elements with similar physicochemical properties.\nWhile computational ligand screening has shown promise toward discovering\nefficient extractants, the complexity of constructing chemically sensible\n3D structures (often by hand), coupled with the high cost of quantum\nchemistry calculations, often limits exploration of the vast ligand\nchemical and conformational space in favor of local exploration around\nknown chemistries. Moreover, metal complexes can have many stable\nconfigurations whose differences in energies exceed the small energy\ndifferences that determine the extractant selectivity for certain\nlanthanides. Because of this difference, incorrect selectivity predictions\ncan be made if the lowest energy coordination complex is not identified\nand modeled. To address this issue, we present a high-throughput computational\nworkflow that automates the construction and quantum mechanical modeling\nof 3D lanthanide-extractant complexes. This approach allows for an\nunbiased search of distinct configurational and compositional variations\nfor each metal, enabling accurate predictions of their solution structures\nand lanthanide selectivity. As showcased by three extractants from\ndiverse chemical categoriesa crown ether, a phenanthroline\nmonocarboxamide, and a malonamideit is found that sampling\nthe lanthanide-ligand configuration space is critical to correctly\npredicting the metal coordination environment and experimental lanthanide\nselectivity trends.