Researchers from Ames Laboratory, Iowa State University and Texas A&M University have 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. They report the model in a paper in Acta Materialia.

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 in 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 approach used in this study is based on machine learning (ML), a form of artificial intelligence (AI) driven by computer algorithms that improve through data usage and experience. The 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 for investigating the thermodynamic and electronic properties of many-body systems. Based on this collection of information, the developed ML model uses regression learning to assess the phase stability of compounds.

Tyler Del Rose, a graduate student at Iowa State University, 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 Prashant Singh, a scientist at Ames Laboratory 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 with new DFT data based on real-time structural and phase information obtained from 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 or 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.

This story is adapted from material from Ames Laboratory, with editorial changes made by Materials Today. The views expressed in this article do not necessarily represent those of Elsevier. Link to original source.

Prashant Singh and Yaroslav Mudryk working on a computer at Ames Laboratory. Photo: U.S. Department of Energy, Ames Laboratory.
Prashant Singh and Yaroslav Mudryk working on a computer at Ames Laboratory. Photo: U.S. Department of Energy, Ames Laboratory.