Computational Synthesis of 2D Materials: A High-throughput Approach to Materials Design
Tara M. Boland, Arunima K. Singh

TL;DR
This paper introduces $Hetero2d$, an open-source high-throughput computational workflow that automates the identification of stable 2D material-substrate heterostructures using DFT simulations, aiding materials design.
Contribution
The paper presents a novel automated workflow, $Hetero2d$, for screening and identifying stable 2D material-substrate heterostructures at large scale.
Findings
Identified substrates stabilizing 2D materials with binding energies of 0.1-0.6 eV/atom.
Demonstrated covalent bonding at 2D-substrate interfaces.
Validated the workflow on four 2D materials with 50 substrates.
Abstract
2D materials find promising applications in next-generation devices, however, large-scale, low-defect, and reproducible synthesis of 2D materials remains a challenging task. To assist in the selection of suitable substrates for the synthesis of as-yet hypothetical 2D materials, we have developed an open-source high-throughput workflow package, , that searches for low-lattice mismatched substrate surfaces for any 2D material and determines the stability of these 2D-substrate heterostructures using density functional theory (DFT) simulations. automates the generation of 2D-substrate heterostructures, the creation of DFT input files, the submission and monitoring of computational jobs on supercomputing facilities, and the storage of relevant parameters alongside the post-processed results in a MongoDB database. We demonstrate the capability of in identifying…
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Taxonomy
Topics2D Materials and Applications · Machine Learning in Materials Science · Electronic and Structural Properties of Oxides
