ArticleJACS Au2022
Advancing Rare-Earth Separation by Machine Learning.
Article in JACS Au, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Toward Transformative Ambient Rare Earth Metal Electrorefining: A Perspective on Electrolyte Design and Circular Processing.ACS materials Au · 2026Review
- Article
- AI and Robotics Advancement in Analytical Mineral Characterization and Mining Processes: A Review and Research Trends Analysis.Topics in current chemistry (Cham) · 2026Review
- Article
- Macrocyclic Chelators for Aqueous Lanthanide Separations via Precipitation: Toward Sustainable Recycling of Rare-Earths from NdFeB Magnets.Journal of the American Chemical Society · 2025Article
- Selective Recovery of Critical Minerals from Simulated Electronic Wastes Via Reaction-Diffusion Coupling.ChemSusChem · 2025Article
- Solvent Screening for Separation Processes Using Machine Learning and High-Throughput Technologies.Chem & bio engineering · 2025Review
- Tetradentate Ligand's Chameleon-Like Behavior Offers Recognition of Specific Lanthanides.Journal of the American Chemical Society · 2024Article
- Insights into coordination and ligand trends of lanthanide complexes from the Cambridge Structural Database.Scientific reports · 2024Article
- Rare Earths-The Answer to Everything.Molecules (Basel, Switzerland) · 2024Review
- Navigating the Expansive Landscapes of Soft Materials: A User Guide for High-Throughput Workflows.ACS polymers Au · 2023Review
- Exploring Optimization of Zeolites as Adsorbents for Rare Earth Elements in Continuous Flow by Machine Learning Techniques.Molecules (Basel, Switzerland) · 2023Article
- Article
- N-oxide ligands for selective separations of lanthanides: insights from computation.RSC advances · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.