Advancing Rare-Earth Separation by Machine Learning

Liu, Tongyu; Johnson, Katherine R.; Jansone-Popova, Santa; Jiang, De-en

doi:10.1021/jacsau.2c00122

Title: Advancing Rare-Earth Separation by Machine Learning

Journal Article · Wed Jun 15 00:00:00 EDT 2022 · JACS Au

DOI:https://doi.org/10.1021/jacsau.2c00122· OSTI ID:1872656

Liu, Tongyu ^[1];

^[2];

^[1]

Department of Chemistry, University of California, Riverside, California 92521, United States
Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States

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.

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Research Organization:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Advanced Manufacturing Office

Grant/Contract Number:: SC00ERKCG21; AC05-00OR22725

OSTI ID:: 1872656

Alternate ID(s):: OSTI ID: 1885271

Journal Information:: JACS Au, Journal Name: JACS Au Vol. 2 Journal Issue: 6; ISSN 2691-3704

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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