High-throughput Identification of Stable 2D Janus-Bulk Material Heterostructures
ORAL · Invited
Abstract
Two-dimensional (2D) Janus materials possess unique properties such as finite out-of-plane dipole moments, Rashba effect, strongly bound excitons, and strong
interaction with light, making them ideal for a wide range of applications from piezoelectric devices to multi-layer 2D heterostructures. Janus MXY materials
are 2D materials where a metal atomic layer M is sandwiched between layers X and Y of two different chalcogen, halogen, or pnictogen atoms. The
properties of Janus materials are prone to alter due to interfacial interactions in a heterostructure. Furthermore, the properties of 2D materials can be
dramatically altered by placing them on substrates. Using our ab-initio workflow package, Hetero2D, we compute the energetic stability, electronic
properties, and charge transfer for ~50 Janus materials on 50 elemental, cubic phase, and metallic substrate materials using van der Waals-corrected
density functional theory. Furthermore, we unravel the structure-property correlations at the 2D Janus-substrate heterostructure interface using machine learning
models.
interaction with light, making them ideal for a wide range of applications from piezoelectric devices to multi-layer 2D heterostructures. Janus MXY materials
are 2D materials where a metal atomic layer M is sandwiched between layers X and Y of two different chalcogen, halogen, or pnictogen atoms. The
properties of Janus materials are prone to alter due to interfacial interactions in a heterostructure. Furthermore, the properties of 2D materials can be
dramatically altered by placing them on substrates. Using our ab-initio workflow package, Hetero2D, we compute the energetic stability, electronic
properties, and charge transfer for ~50 Janus materials on 50 elemental, cubic phase, and metallic substrate materials using van der Waals-corrected
density functional theory. Furthermore, we unravel the structure-property correlations at the 2D Janus-substrate heterostructure interface using machine learning
models.
*The authors thank the National Science Foundation grant number DMR-1906030 and start-up funds from Arizona State University. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), supported by National Science Foundation grant number TG-DMR150006. The authors acknowledge Research Computing at Arizona State University for providing HPC resources that have contributed to the research results reported within this paper. This research also used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.
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Presenters
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Arunima Singh
- Arizona State University