Artificial Intelligence Paves the Way to Discovering New Rare-earth Compounds
upstart writes:
Artificial intelligence paves the way to discovering new rare-earth compounds:
[...] Rare earth elements have a wide range of uses including clean energy technologies, energy storage, and permanent magnets. Discovery of new rare-earth compounds is part of a larger effort by scientists to expand access to these materials.
The present approach is based on machine learning (ML), a form of artificial intelligence (AI), which is driven by computer algorithms that improve through data usage and experience. Researchers used the upgraded Ames Laboratory Rare Earth database (RIC 2.0) and high-throughput density-functional theory (DFT) to build the foundation for their ML model.
High-throughput screening is a computational scheme that allows a researcher to test hundreds of models quickly. DFT is a quantum mechanical method used to investigate thermodynamic and electronic properties of many body systems. Based on this collection of information, the developed ML model uses regression learning to assess phase stability of compounds.
[...] "It's not really meant to discover a particular compound," Mudryk said. "It was, how do we design a new approach or a new tool for discovery and prediction of rare earth compounds? And that's what we did."
Mudryk emphasized that this work is just the beginning. The team is exploring the full potential of this method, but they are optimistic that there will be a wide range of applications for the framework in the future.
Journal Reference:
Redirecting, (DOI: 10.1016/j.actamat.2022.117759)
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