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.
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Presenters
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Arunima Singh
Arizona State University
Authors
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Arunima Singh
Arizona State University
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Tara Boland
DTU