Advancing Rare-Earth Separation by Machine Learning

Constituting the
bulk of rare-earth elements, lanthanides need
to be separated to fully realize their potential as critical materials
in many important technologies. The discovery of new ligands for improving
rare-earth separations by solvent extraction, the most practical rare-earth
separation process, is still largely based on trial and error, a low-throughput
and inefficient approach. A predictive model that allows high-throughput
screening of ligands is needed to identify suitable ligands to achieve
enhanced separation performance. Here, we show that deep neural networks,
trained on the available experimental data, can be used to predict
accurate distribution coefficients for solvent extraction of lanthanide
ions, thereby opening the door to high-throughput screening of ligands
for rare-earth separations. One innovative approach that we employed
is a combined representation of ligands with both molecular physicochemical
descriptors and atomic extended-connectivity fingerprints, which greatly
boosts the accuracy of the trained model. More importantly, we synthesized
four new ligands and found that the predicted distribution coefficients
from our trained machine-learning model match well with the measured
values. Therefore, our machine-learning approach paves the way for
accelerating the discovery of new ligands for rare-earth separations.