TODAY: Fall 2023 Seminar Series: Department of Physics, Astronomy & Geosciences
Friday, November 3, 2023
11:00am – 12:00pm
SC 1230
Data-driven Exploration of New Two-Dimensional Magnets Using Graph Neural Networks
Speaker: Ahmed ElRashidy, TU Applied Physics MS
Two-dimensional (2D) magnets have a transformative potential in spintronics applications. 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. We have identified 158 potential materials which 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 to aid discovery of new 2D magnets.
Categorised in: General
This post was written by Charles, Amanda G.