UC Riverside

Constituting the bulk of rare-earth elements, lanthanides (Ln) need to be separated to fully realize their potential as critical materials in many important technologies. Solvent extraction is the most extensively used process to separate lanthanides on an industrial scale. Recently, preorganized ligands such as bis-lactam-1,10-phenanthroline show unique selectivity trends across the lanthanide series, indicating the synergistic effects of both N and O donors in complexing with lanthanides. So, we examined mixed N, O-donor ligands containing pyridinic N and N-oxide groups and evaluated their relative aqueous La(III)/Ln(III) selectivity by computing free energy changes for the exchange reaction between the designed ligands and a reference ligand via density functional theory (DFT). Three novel ligands show promise as excellent extractant agents in selectively separating trivalent lanthanides. However, no matter our DFT investigations or other discovery of new ligands in rare-earth separations by solvent extraction, they are still largely based on trial and error, a low-throughput and inefficient approach. As a result, deep neural networks on the available experimental data of distribution coefficients measured for hundreds of ligands for 14 Ln(III) ions have been trained to accurately and quickly predict their distribution coefficients for a given ligand and the extraction conditions. Four new-synthesized ligands were found that their predicted distribution coefficients from our trained machine-learning model match well with their experimental values. Then, this trained model was applied to some large ligand databases automatically generated by molecule generation tools based on string-based representations. Several hexalkyl-nitrilotriacetamide ligands were screened out with high potential in selective rare-earth separations. Therefore, our machine-learning approach paves the way for accelerating the discovery of new ligands for rare-earth separations. In additional to rare-earth elements, some theoretical insights towards atomically precise ligand-protected nanoclusters by first principles were performed. All work in this dissertation aim at chemically understanding the interactions between metals and organic ligands by different computational approaches.