Exploring different search approaches to discover donor molecules for organic solar cells

JSON Export

{
  "updated": "2024-11-04T08:31:33.970118+00:00", 
  "id": "2338", 
  "metadata": {
    "publication_date": "Nov 04, 2024, 09:31:33", 
    "edited_by": 576, 
    "doi": "10.24435/materialscloud:t7-5a", 
    "references": [
      {
        "citation": "M. Azzouzi, S. Benett, V. Posligua, R. Bondesan, M. Zwijnenburg, K. Jelfs, Submitted to Digital Chemistry.", 
        "type": "Preprint"
      }
    ], 
    "description": "Identifying organic molecules with desirable properties from the extensive chemical space can be challenging, particularly when property evaluation methods are time-consuming and resource intensive. In this study, we illustrate this challenge by exploring the chemical space of large oligomers, constructed from monomeric building blocks, for potential use in organic photovoltaics (OPV). To facilitate this exploration, we developed a Python package called stk-search, which employs a building block approach. For this purpose, we developed a python package to search the chemical space using a building block approach: stk-search. We use stk-search (GitHub link) to compare a variety of search algorithms, including those based upon Bayesian optimization and evolutionary approaches. Initially, we evaluated and compared the performance of different search algorithms within a precomputed search space. We then extended our investigation to the vast chemical space of molecules formed of 6 building blocks (6-mers), comprising over 10<sup>14</sup> molecules. Notably, while some algorithms show only marginal improvements over a random search approach in a relatively small, precomputed, search space, their performance in the larger chemical space is orders of magnitude better. Specifically, Bayesian optimization identified a thousand times more promising molecules with the desired properties compared to random search, using the same computational resources. \nThis record contains the dataset generated during the exploration of the space of molecules formed of 6 building blocks for application as donor molecules for OPV application, with calculated properties such as Ionisation potential, excited state energy and oscillator strength.", 
    "contributors": [
      {
        "email": "mohammed.azzouzi@epfl.ch", 
        "affiliations": [
          "Institut des Sciences et Ing\u00e9nierie Chimiques, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL)", 
          "Department of Chemistry, Imperial College London, White City Campus, W12 0BZ, London United Kingdom"
        ], 
        "givennames": "Mohammed", 
        "familyname": "Azzouzi"
      }, 
      {
        "affiliations": [
          "Department of Chemistry, Imperial College London, White City Campus, W12 0BZ, London United Kingdom"
        ], 
        "givennames": "Steven", 
        "familyname": "Benett"
      }, 
      {
        "affiliations": [
          "Department of Chemistry, Imperial College London, White City Campus, W12 0BZ, London United Kingdom"
        ], 
        "givennames": "Victor", 
        "familyname": "Posligua"
      }, 
      {
        "affiliations": [
          "Department of Computing, Imperial College London, London SW7 2AZ, United Kingdom"
        ], 
        "givennames": "Roberto", 
        "familyname": "Bondesan"
      }, 
      {
        "affiliations": [
          "Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, United Kingdom"
        ], 
        "givennames": "Martijn", 
        "familyname": "Zwijnenburg"
      }, 
      {
        "email": "K.jelfs@imperial.ac.uk", 
        "affiliations": [
          "Department of Chemistry, Imperial College London, White City Campus, W12 0BZ, London United Kingdom"
        ], 
        "givennames": "Kim", 
        "familyname": "Jelfs"
      }
    ], 
    "is_last": true, 
    "license_addendum": null, 
    "_files": [
      {
        "description": "Zip file with the json files necessary to load the precursor database into a mongo db database using the notebook: 00_load_database_to_mongodb.ipynb", 
        "checksum": "md5:1c86c777fd71fa769afd68eb377b2229", 
        "size": 375029, 
        "key": "data_precurosr_db.7z"
      }, 
      {
        "description": "Notebook to load the data into a mongodb database", 
        "checksum": "md5:9b6e5eb0d21f86f79dcd9488b211e052", 
        "size": 43084, 
        "key": "00_load_database_to_mongodb.ipynb"
      }, 
      {
        "description": "File containing the json files for the constructed molecules database.", 
        "checksum": "md5:ac0e367515c0949dd49c27629934e5ba", 
        "size": 773037291, 
        "key": "stk_constructed.zip"
      }, 
      {
        "description": "Description of the files in the record", 
        "checksum": "md5:57878a4831e4c60a0dfd459db4068467", 
        "size": 624, 
        "key": "read_me.txt"
      }
    ], 
    "version": 1, 
    "id": "2338", 
    "conceptrecid": "2337", 
    "owner": 1490, 
    "license": "Creative Commons Attribution 4.0 International", 
    "mcid": "2024.178", 
    "_oai": {
      "id": "oai:materialscloud.org:2338"
    }, 
    "status": "published", 
    "title": "Exploring different search approaches to discover donor molecules for organic solar cells", 
    "keywords": [
      "Organic solar cells", 
      "bayesian optimisation", 
      "genetic algorithm", 
      "building block approach", 
      "Representation learning"
    ]
  }, 
  "revision": 9, 
  "created": "2024-09-19T09:21:35.398421+00:00"
}