Deep Generative Model of Interfacial Structures in Phase Transformation of an MoWSe<sub>2</sub> Monolayer
ORAL
Abstract
Optical and electrical properties of two-dimensional layered materials can be tuned by mechanical straining, which induces transformations from semiconducting to metallic phases. We use deep generative variational autoencoder (VAE) model, trained by molecular dynamics simulation data of dynamic fracture in an MoWSe2 monolayer, to predict transition pathways consisting of novel intermediate structures (a and b) between the semiconducting (2H) and metallic (1T) phases. In addition, a conditional variational autoencoder (CVAE) is used to generate intermediate structures such as a or b , and defects. Structures synthesized from VAE and CVAE are validated by quantum simulations based on density functional theory. Quantum simulations show that structures generated by VAE and CVAE are stable and can be used for nanoelectronics applications.
*The work was supported by the grant DE-SC0018195 funded by the U.S. Department of Energy, Office of Science. Simulations were performed at the Argonne Leadership Computing Facility under the DOE INCITE program and at the Center for High Performance Computing of the University of Southern California.
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Presenters
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Pankaj Rajak
- University of Southern California
- Argonne national laboratory
- Argonne Leadership Computing Facility, Argonne National Laboratory
- Physics & Astronomy, University of Southern California