Polygonal tessellations as predictive models of molecular monolayers

Regös, Krisztina and Pawlak, Rémy and Wang, Xing and Meyer, Ernst and Decurtins, Silvio and Domokos, Gábor and Novoselov, Kostya S. and Liu, Shi-Xia and Aschauer, Ulrich. (2023) Polygonal tessellations as predictive models of molecular monolayers. Proceedings of the National Academy of Sciences of the United States of America, 120 (16). e2300049120.

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Official URL: https://edoc.unibas.ch/94496/

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Molecular self-assembly plays a very important role in various aspects of technology as well as in biological systems. Governed by covalent, hydrogen or van der Waals interactionsâEuro"self-assembly of alike molecules results in a large variety of complex patterns even in two dimensions (2D). Prediction of pattern formation for 2D molecular networks is extremely important, though very challenging, and so far, relied on computationally involved approaches such as density functional theory, classical molecular dynamics, Monte Carlo, or machine learning. Such methods, however, do not guarantee that all possible patterns will be considered and often rely on intuition. Here, we introduce a much simpler, though rigorous, hierarchical geometric model founded on the mean-field theory of 2D polygonal tessellations to predict extended network patterns based on molecular-level information. Based on graph theory, this approach yields pattern classification and pattern prediction within well-defined ranges. When applied to existing experimental data, our model provides a different view of self-assembled molecular patterns, leading to interesting predictions on admissible patterns and potential additional phases. While developed for hydrogen-bonded systems, an extension to covalently bonded graphene-derived materials or 3D structures such as fullerenes is possible, significantly opening the range of potential future applications.
Faculties and Departments:05 Faculty of Science > Departement Physik > Physik > Nanomechanik (Meyer)
UniBasel Contributors:Pawlak, Rémy and Meyer, Ernst
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:National Academy of Sciences
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:08 May 2023 13:29
Deposited On:08 May 2023 13:29

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