Toward accelerating rare-earth metal extraction using equivariant neural networks
A high-throughput workflow leveraging equivariant GNNs and a diverse dataset of rare-earth complexes to predict binding affinities and accelerate critical metal separation.
Authors
- Wibe A. de Jong (ORCID: https://orcid.org/0000-0002-7114-8315)
- Ankur K. Gupta (ORCID: https://orcid.org/0000-0002-3128-9535)
- C. Hetherington
Institutions
- Lawrence Berkeley National Laboratory (US)
Publication Details
- Journal
- Digital Discovery
- Published
- 2025-11-26
- DOI
- https://doi.org/10.1039/d5dd00286a
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- U.S. Department of Energy
- National Energy Research Scientific Computing Center
- Office of Science
- Basic Energy Sciences
- Chemical Sciences, Geosciences, and Biosciences Division
- Lawrence Berkeley National Laboratory