On the Importance of Configuration Search to the Predictivity of Lanthanide Selectivity

The lanthanide elements are crucial components in numerous
technologies,
yet their industrial production through liquid–liquid extraction
continues to be economically and environmentally costly due to the
challenge of separating elements with similar physicochemical properties.
While computational ligand screening has shown promise toward discovering
efficient extractants, the complexity of constructing chemically sensible
3D structures (often by hand), coupled with the high cost of quantum
chemistry calculations, often limits exploration of the vast ligand
chemical and conformational space in favor of local exploration around
known chemistries. Moreover, metal complexes can have many stable
configurations whose differences in energies exceed the small energy
differences that determine the extractant selectivity for certain
lanthanides. Because of this difference, incorrect selectivity predictions
can be made if the lowest energy coordination complex is not identified
and modeled. To address this issue, we present a high-throughput computational
workflow that automates the construction and quantum mechanical modeling
of 3D lanthanide-extractant complexes. This approach allows for an
unbiased search of distinct configurational and compositional variations
for each metal, enabling accurate predictions of their solution structures
and lanthanide selectivity. As showcased by three extractants from
diverse chemical categoriesa crown ether, a phenanthroline
monocarboxamide, and a malonamideit is found that sampling
the lanthanide-ligand configuration space is critical to correctly
predicting the metal coordination environment and experimental lanthanide
selectivity trends.