Nanotechnology Now – Press Release: Rensselaer researchers use artificial intelligence to find new materials for advanced computing Trevor Rhone uses AI to identify two-dimensional van der Waals magnets
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Home > press > Rensselaer researcher uses artificial intelligence to discover new materials for advanced computing Trevor Rhone uses AI to identify two-dimensional van der Waals magnets
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| Rensselaer Polytechnic Institute CREDIT Trevor David Rhone |
Abstract:
A research team led by Trevor David Rhone of Rensselaer Polytechnic Institute, assistant professor in the Departments of Physics, Applied Physics, and Astronomy, has identified a novel van der Waals magnet (vdW) using cutting-edge tools in artificial intelligence (AI). Specifically, the team identified vdW transition metal halide materials with large magnetic moments that were predicted to be chemically stable using semi-supervised learning. These two-dimensional (2D) vdW magnets have potential applications in data storage, spintronics, and even quantum computing.
Rensselaer researcher uses artificial intelligence to find new materials for advanced computing Trevor Rhone uses AI to identify two-dimensional van der Waals magnets
Troy, New York | Posted on May 12, 2023
Rhone specializes in utilizing 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. His team’s latest research was recently featured on the cover of Advanced Theory and Simulations.
2D matter, which can be as thin as a single atom, was only discovered in 2004 and has become the subject of great scientific curiosity due to its unpredictable nature. 2D magnets are important because the long-range magnetic sequence persists when thinned into one or more layers. This is due to magnetic anisotropy. This interaction with magnetic anisotropy and low dimensions can give rise to exotic spin degrees of freedom, such as spin textures that can be used in the development of quantum computing architectures. 2D magnets also span a wide range of electronic properties and can be used in high performance and energy efficient devices.
Rhone and team combined high-throughput density functional theory (DFT) calculations, to determine vdW material properties, with AI to implement a form of machine learning called semi-supervised learning. Semi-supervised learning uses a combination of labeled and unlabeled data to identify patterns in the data and make predictions. Semi-supervised learning mitigates a key challenge in machine learning – the scarcity of labeled data.
“Using AI saves time and money,” said Rhone. “A typical material discovery process requires expensive simulations on supercomputers that can take months. Laboratory experiments can take longer and can be more expensive. The AI approach has the potential to speed up the materials discovery process.”
Using an initial subset of 700 DFT calculations on a supercomputer, an AI model is trained that can predict the properties of thousands of candidate materials within milliseconds on a laptop. The team then identified a promising vdW material candidate with a large magnetic moment and low formation energy. Low formation energy is an indicator of chemical stability, which is an important requirement for synthesizing materials in laboratories and subsequent industrial applications.
“Our framework can easily be applied to explore materials with different crystal structures as well,” said Rhone. “Prototyping of mixed crystal structures, such as the transition metal halide and transition metal trichalkogenide data sets, can also be explored with this framework.”
“Dr. The Rhone’s application of AI to the field of materials science continues to produce exciting results,” said Curt Breneman, dean of the Rensselaer School of Science. “He has not only accelerated our understanding of 2D materials having new properties, but his findings and methods will likely contribute to new quantum computing technologies.”
The Rhone was joined in research by Romakanta Bhattarai and Haralambos Gavras of Renselaer; Bethany Lusch and Misha Salim from Argonne National Laboratory; Marios Mattheakis, Daniel T. Larson, and Efthimios Kaxiras of Harvard University; and Yoshiharu Krockenberger from the NTT Basic Research Laboratory.
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About the Rensselaer Polytechnic Institute
Founded in 1824, Rensselaer Polytechnic Institute is America’s first technological research university. Rensselaer includes five schools, more than 30 research centers, more than 140 academic programs including 25 new ones, and a vibrant community of more than 6,800 students and 104,000 living alumni. Rensselaer faculty and alumni include more than 155 members of the National Academies, six members of the National Inventors Hall of Fame, six National Medal of Technology winners, five National Medal of Science winners, and a Nobel Prize winner in Physics. With nearly 200 years of experience advancing science and technology, Rensselaer remains focused on tackling global challenges with a spirit of ingenuity and collaboration. To learn more, please visit www.rpi.edu.
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Contact:
Katie Malatino
Rensselaer Polytechnic Institute
Tue: 838-240-5691
@rpi
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