ArticleScientific data2023
High throughput calculations for a dataset of bilayer materials.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Deep learning methods for 2D material electronic properties.Digital discovery · 2026Review
- Band structure database of layered intercalation compounds with various intercalant atoms and layered hosts.Scientific data · 2024Article
- High throughput calculations for a dataset of bilayer materials.Scientific data · 2023Article
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Authors and funding
2 authors.
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Abstract
Bilayer materials made of 2D monolayers are emerging as new systems creating diverse opportunities for basic research and applications in optoelectronics, thermoelectrics, and topological science among others. Herein, we present a computational bilayer materials dataset containing 760 structures with their structural, electronic, and transport properties. Different stacking patterns of each bilayer have been framed by analyzing their monolayer symmetries. Density functional theory calculations including van der Waals interactions are carried out for each stacking pattern to evaluate the corresponding ground states, which are correctly identified for experimentally synthesized transition metal dichalcogenides, graphene, boron nitride, and silicene. Binding energies and interlayer charge transfer are evaluated to analyze the interlayer coupling strength. Our dataset can be used for materials screening and data-assisted modeling for desired thermoelectric or optoelectronic applications.
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Registered trials
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