Bulletin of the American Physical Society
APS March Meeting 2024
Monday–Friday, March 4–8, 2024; Minneapolis & Virtual
Session N25: Low Dimensional Magnetism: 2D Magnets
11:30 AM–2:18 PM,
Wednesday, March 6, 2024
Room: 101F
Sponsoring
Unit:
GMAG
Chair: David Mandrus, University of Tennessee
Abstract: N25.00013 : Data-driven Exploration of New Two-Dimensional Magnets Using Graph Neural Networks*
1:54 PM–2:06 PM
Presenter:
Ahmed Elrashidy
(Towson University)
Authors:
Ahmed Elrashidy
(Towson University)
Jia-An Yan
(Towson Uninversity)
James Della-Giustina
(Towson University)
Two-dimensional (2D) magnets have a transformative potential in spintronics applications. In this study, we employ Graph Neural Networks (GNNs) to discover novel 2D magnetic materials. Using data from the Materials Project database and the Computational 2D materials database (C2DB), we train three GNN architectures on a dataset of approximately 1,200 magnetic monolayers with energy above hull less than 0.3 eV. Our Crystal Diffusion Variational Auto Encoder (CDVAE) generates around 11,000 material candidates. Subsequent training on two Atomistic Line Graph Neural Networks (ALIGNN) achieves a 93% accuracy in predicting magnetic monolayers and a mean average error of 0.04 eV for energy above hull prediction. After narrowing down candidates based on magnetic likelihood and predicted energy above hull, and constraining the atom count in the monolayer to four or fewer, we identified 160 potential materials. These are validated using density-functional theory (DFT) to confirm their magnetic and energetic favorability. Our approach offers a methodical way to explore and predict potential 2D magnetic materials, contributing to ongoing computational and experimental efforts aimed at the discovery of new 2D magnets.
**This work used EXPANSE at the San Diego Supercomputing Center (SDSC) through allocation PHY220161 from the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program. We acknowledge support from the NSF grant DMR 1709781 and support from the Fisher General Endowment and SET grants from the Jess and Mildred Fisher College of Science and Mathematics at Towson University
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