Absorption $\text{versus}$ Adsorption: High-Throughput Computation of Impurities in 2D Materials

Abstract

Doping of a two-dimensional (2D) material by impurity atoms occurs \textit{via} two distinct mechanisms: absorption of the dopants by the 2D crystal or adsorption on its surface. To distinguish the relevant mechanism, we systematically dope 53 experimentally synthesized 2D monolayers by 65 different chemical elements in both absorption and adsorption sites. The resulting 17,598 doped monolayer structures were generated using the newly developed ASE \texttt{DefectBuilder} -- a Python tool to set up point defects in 2D and bulk materials -- and subsequently relaxed by an automated high-throughput density functional theory (DFT) workflow. We find that interstitial positions are preferred for small dopants with partially filled valence electrons in host materials with large lattice parameters. On the contrary, adatoms are favored for dopants with a low number of valence electrons due to lower coordination of adsorption sites compared to interstitials. The relaxed structures, characterization parameters, defect formation energies, and magnetic moments (spins) are available in an open database to help advance our understanding of defects in 2D materials.

arXiv commentJoel Davidsson and Fabian Bertoldo contributed equally

References in corpus (10)

• 2D materials and van der Waals heterostructures
• Bandgap Engineering of Strained Monolayer and Bilayer MoS2
• Tuning the graphene work function by electric field effect
• The Computational 2D Materials Database: High-Throughput Modeling and Discovery of Atomically Thin Crystals
• Van der Waals heterostructures for spintronics and opto-spintronics
• Recent Progress of the Computational 2D Materials Database (C2DB)
• Giant Stark splitting of an exciton in bilayer MoS${}_2$
• Implantation and atomic scale investigation of self-interstitials in graphene
• ADAQ: Automatic workflows for magneto-optical properties of point defects in semiconductors
• Database-driven High-Throughput Calculations and Machine Learning Models for Materials Design

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• Quantum Defects in 2D Transition Metal Dichalcogenides for Terahertz Technologies
• NV-like Defects More Common Than Four-Leaf Clovers: A Perspective on High-Throughput Point Defect Data