Polygonal tessellations as predictive models of molecular monolayers

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{
  "revision": 4, 
  "id": "1705", 
  "created": "2023-03-22T15:22:27.303124+00:00", 
  "metadata": {
    "doi": "10.24435/materialscloud:e4-h1", 
    "status": "published", 
    "title": "Polygonal tessellations as predictive models of molecular monolayers", 
    "mcid": "2023.48", 
    "license_addendum": null, 
    "_files": [
      {
        "description": "Raw data and code needed to reproduce data in section S3", 
        "key": "tesselation.tar.gz", 
        "size": 554291, 
        "checksum": "md5:17dee26ac8f45d047a4da47e9f18f1e4"
      }
    ], 
    "owner": 646, 
    "_oai": {
      "id": "oai:materialscloud.org:1705"
    }, 
    "keywords": [
      "Molecular self assembly", 
      "Tesselation", 
      "2D monolayer"
    ], 
    "conceptrecid": "1704", 
    "is_last": true, 
    "references": [
      {
        "type": "Preprint", 
        "doi": "10.48550/arXiv.2208.03964", 
        "url": "https://arxiv.org/abs/2208.03964", 
        "citation": "K. Reg\u0151s, R. Pawlak, X. Wang, E. Meyer, S. Decurtins, G. Domokos, K. S. Novoselov, S.-X. Liu, U. Aschauer, arXiv:2208.03964 [cond-mat.mtrl-sci], (2022)"
      }
    ], 
    "publication_date": "Mar 22, 2023, 16:46:42", 
    "license": "Creative Commons Attribution 4.0 International", 
    "id": "1705", 
    "description": "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 interactions - 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 an entirely new 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.", 
    "version": 1, 
    "contributors": [
      {
        "affiliations": [
          "Department of Morphology and Geometric Modeling, and ELKH-BME\nMorphodynamics Research Group, Budapest University of Technology and Economics\nH-1111 Budapest, Hungary"
        ], 
        "familyname": "Reg\u0151s", 
        "givennames": "Krisztina"
      }, 
      {
        "affiliations": [
          "Department of Physics, University of Basel 4056 Basel, Switzerland"
        ], 
        "familyname": "Pawlak", 
        "givennames": "R\u00e9my"
      }, 
      {
        "affiliations": [
          "Department of Chemistry, Biochemistry and Pharmacy, University of Bern, 3012 Bern, Switzerland"
        ], 
        "familyname": "Wang", 
        "givennames": "Xing"
      }, 
      {
        "affiliations": [
          "Department of Physics, University of Basel 4056 Basel, Switzerland"
        ], 
        "familyname": "Meyer", 
        "givennames": "Ernst"
      }, 
      {
        "affiliations": [
          "Department of Chemistry, Biochemistry and Pharmacy, University of Bern, 3012 Bern, Switzerland"
        ], 
        "familyname": "Decurtins", 
        "givennames": "Silvio"
      }, 
      {
        "affiliations": [
          "Department of Morphology and Geometric Modeling, and ELKH-BME\nMorphodynamics Research Group, Budapest University of Technology and Economics\nH-1111 Budapest, Hungary"
        ], 
        "familyname": "Domokos", 
        "givennames": "G\u00e1bor"
      }, 
      {
        "affiliations": [
          "Institute for Functional Intelligent Materials, National University of Singapore, 117544, Singapore"
        ], 
        "familyname": "Novoselov", 
        "givennames": "Kostya S."
      }, 
      {
        "affiliations": [
          "Department of Chemistry, Biochemistry and Pharmacy, University of Bern, 3012 Bern, Switzerland"
        ], 
        "familyname": "Liu", 
        "givennames": "Shi-Xia"
      }, 
      {
        "email": "ulrich.aschauer@unibe.ch", 
        "affiliations": [
          "Department of Chemistry, Biochemistry and Pharmacy, University of Bern, 3012 Bern, Switzerland"
        ], 
        "familyname": "Aschauer", 
        "givennames": "Ulrich"
      }
    ], 
    "edited_by": 576
  }, 
  "updated": "2023-03-22T15:46:42.275835+00:00"
}