ArticleACS nano2023
Expansion of the Materials Cloud 2D Database.
Article in ACS nano, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- High-Throughput Prediction of Exfoliable Non-van der Waals Materials from a Universal Potential.Nature communications · 2026Article
- Artificial intelligence for design strategies of tissue engineering materials.Fundamental research · 2026Review
- A self-reliance framework for identifying strategic advanced materials.Nature communications · 2026Article
- Search for Low-Periodic Substructures in Crystalline Solids: A Novel Approach.ACS applied materials & interfaces · 2026Article
- Article
- A universal machine learning model for the electronic density of states.Digital discovery · 2026Article
- Article
- MC3D: the Materials Cloud computational database of experimentally known stoichiometric inorganics.Digital discovery · 2026Article
- Quasiparticle Effects and Strong Excitonic Features in Exfoliable 1D Semiconducting Materials.ACS nano · 2026Article
- Deep learning methods for 2D material electronic properties.Digital discovery · 2026Review
- Accurate and efficient protocols for high-throughput first-principles materials simulations.npj computational materials · 2026Article
- PET-MAD as a lightweight universal interatomic potential for advanced materials modeling.Nature communications · 2025Article
- Massive Atomic Diversity: a compact universal dataset for atomistic machine learning.Scientific data · 2025Article
- Extreme Anharmonicity and Thermal Contraction of One-Dimensional Wires.Nano letters · 2025Article
- Electronic properties and circuit applications of networks of electrochemically exfoliated 2D nanosheets.Nature communications · 2025Article
- Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange.Digital discovery · 2024Article
- Inorganic Metal Thiocyanates.Inorganic chemistry · 2024Review
- Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures.Advanced materials (Deerfield Beach, Fla.) · 2024Review
- Enhanced spin Hall ratio in two-dimensional semiconductors.npj computational materials · 2024Article
- Expansion of the Materials Cloud 2D Database.ACS nano · 2023Article
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5 authors.
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Abstract
Two-dimensional (2D) materials are among the most promising candidates for beyond-silicon electronic, optoelectronic, and quantum computing applications. Recently, their recognized importance sparked a push to discover and characterize novel 2D materials. Within a few years, the number of experimentally exfoliated or synthesized 2D materials went from a few to more than a hundred, with the number of theoretically predicted compounds reaching a few thousand. In 2018 we first contributed to this effort with the identification of 1825 compounds that are either easily (1036) or potentially (789) exfoliable from experimentally known 3D compounds. Here, we report on a major expansion of this 2D portfolio thanks to the extension of the screening protocol to an additional experimental database (MPDS) as well as the updated versions of the two databases (ICSD and COD) used in our previous work. This expansion leads to the discovery of an additional 1252 monolayers, bringing the total to 3077 compounds and, notably, almost doubling the number of easily exfoliable materials to 2004. We optimize the structural properties of all these monolayers and explore their electronic structure with a particular emphasis on those rare large-bandgap 2D materials that could be precious in isolating 2D field-effect-transistor channels. Finally, for each material containing up to 6 atoms per unit cell, we identify the best candidates to form commensurate heterostructures, balancing requirements on supercell size and minimal strain.
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