Artificial intelligence paves the way to discovering new rare-earth
compounds

Date:
March 18, 2022
Source:
DOE/Ames Laboratory
Summary:
Artificial intelligence advances how scientists explore materials.

Researchers trained a machine-learning (ML) model to assess the
stability of rare-earth compounds. The framework they developed
builds on current state-of-the-art methods for experimenting with
compounds and understanding chemical instabilities.



FULL STORY ========================================================================== Artificial intelligence advances how scientists explore
materials. Researchers from Ames Laboratory and Texas A&M University
trained a machine-learning (ML) model to assess the stability of
rare-earth compounds. This work was supported by Laboratory Directed
Research and Development Program (LDRD) program at Ames Laboratory. The framework they developed builds on current state-of-the-art methods for experimenting with compounds and understanding chemical instabilities.


==========================================================================
Ames Lab has been a leader in rare-earths research since the middle
of the 20th century. 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.

Tyler Del Rose, an Iowa State University graduate student, conducted
much of the foundational research needed for the database by writing
algorithms to search the web for information to supplement the database
and DFT calculations.

He also worked on experimental validation of the AI predictions and
helped to improve the ML based models by ensuring they are representative
of reality.

"Machine learning is really important here because when we are talking
about new compositions, ordered materials are all very well known to
everyone in the rare earth community," said Ames Laboratory Scientist
Prashant Singh, who led the DFT plus machine learning effort with
Guillermo Vazquez and Raymundo Arroyave. "However, when you add disorder
to known materials, it's very different. The number of compositions
becomes significantly larger, often thousands or millions, and you cannot investigate all the possible combinations using theory or experiments."
Singh explained that the material analysis is based on a discrete
feedback loop in which the AI/ML model is updated using new DFT database
based on real-time structural and phase information obtained from our experiments. This process ensures that information is carried from one
step to the next and reduces the chance of making mistakes.

Yaroslav Mudryk, the project supervisor, said that the framework was
designed to explore rare earth compounds because of their technological importance, but its application is not limited to rare-earths
research. The same approach can be used to train an ML model to predict magnetic properties of compounds, process controls for transformative manufacturing, and optimize mechanical behaviors.

"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.


========================================================================== Story Source: Materials provided by DOE/Ames_Laboratory. Note: Content
may be edited for style and length.


========================================================================== Journal Reference:
1. Prashant Singh, Tyler Del Rose, Guillermo Vazquez, Raymundo
Arroyave,
Yaroslav Mudryk. Machine-learning enabled thermodynamic model for
the design of new rare-earth compounds. Acta Materialia, 2022;
229: 117759 DOI: 10.1016/j.actamat.2022.117759 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/03/220318161444.htm

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