Trevor David Rhone, assistant professor in the Department of Physics, Applied Physics, and Astronomy at Rensselaer Polytechnic Institute. Photo: Rensselaer Polytechnic Institute.A team of researchers led by Trevor David Rhone, assistant professor in the Department of Physics, Applied Physics, and Astronomy at Rensselaer Polytechnic Institute, has used cutting-edge tools in artificial intelligence (AI) to identify novel van der Waals (vdW) magnets. Specifically, the team used semi-supervised learning to identify transition metal halide vdW materials with large magnetic moments that are predicted to be chemically stable. These two-dimensional (2D) vdW magnets have potential applications in data storage, spintronics and even quantum computing.
Rhone specializes in harnessing materials informatics to discover new materials with unexpected properties that advance science and technology. Materials informatics is an emerging field of study at the intersection of AI and materials science. Rhone and his team report this latest work in a paper in Advanced Theory and Simulations.
Only discovered in 2004, 2D materials, which can be as thin as a single atom, have been the subject of great scientific curiosity because of their unexpected properties. Magnetic 2D materials are significant because their long-range magnetic ordering persists when they are thinned down to one or a few layers. This is due to magnetic anisotropy. The interplay between this magnetic anisotropy and low dimensionality could give rise to exotic spin degrees of freedom, including spin textures that can be used in the development of quantum computing architectures. What is more, 2D magnets span the full range of electronic properties and can be used in high-performance and energy-efficient devices.
Rhone and his team combined high-throughput density functional theory (DFT) calculations, to determine the properties of vdW materials, with AI to implement a form of machine learning called semi-supervised learning. This uses a combination of labeled and unlabeled data to identify patterns in data and make predictions. Semi-supervised learning mitigates a major challenge in machine learning – the scarcity of labeled data.
“Using AI saves time and money,” said Rhone. “The typical materials-discovery process requires expensive simulations on a supercomputer that can take months. Lab experiments can take even longer and can be more expensive. An AI approach has the potential to speed up the materials discovery process.”
Using an initial subset of 700 DFT calculations on a supercomputer, Rhone and his team trained an AI model to predict the properties of many thousands of materials candidates in milliseconds on a laptop. The team then identified promising candidate vdW materials with large magnetic moments and low formation energy. Low formation energy is an indicator of chemical stability, which is an important requirement for synthesizing the material in a laboratory and subsequent industrial applications.
“Our framework can easily be applied to explore materials with different crystal structures, as well,” said Rhone. “Mixed crystal structure prototypes, such as a data set of both transition metal halides and transition metal trichalcogenides, can also be explored with this framework.”
“Dr. Rhone’s application of AI to the field of materials science continues to produce exciting results,” said Curt Breneman, dean of Rensselaer’s School of Science. “He has not only accelerated our understanding of 2D materials that have novel properties, but his findings and methods are likely to contribute to new quantum-computing technologies.”
This story is adapted from material from Rensselaer Polytechnic Institute, with editorial changes made by Materials Today. The views expressed in this article do not necessarily represent those of Elsevier. Link to original source.