Inverse design of singlet fission materials with uncertainty-controlled genetic optimization

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{
  "metadata": {
    "edited_by": 576, 
    "owner": 1412, 
    "_oai": {
      "id": "oai:materialscloud.org:2256"
    }, 
    "description": "Singlet fission has shown potential for boosting the power conversion efficiency of solar cells, but the scarcity of suitable molecular materials hinders its implementation. We introduce an uncertainty-controlled genetic algorithm (ucGA) based on ensemble machine learning predictions from different molecular representations that concurrently optimizes excited state energies, synthesizability, and singlet exciton size for the discovery of singlet fission materials. We show that uncertainty in the model predictions can control how far the genetic optimization moves away from previously known molecules.\nRunning the ucGA in an exploitative setup performs local optimization on variations of known singlet fission scaffolds, such as acenes. In an explorative mode, hitherto unknown candidates displaying excellent excited state properties for singlet fission are generated. We suggest a class of heteroatom-rich mesoionic compounds as acceptors for charge-transfer mediated singlet fission. When included in larger conjugated donor-acceptor systems, these units exhibit strong localization of the triplet state, favorable diradicaloid character and suitable triplet energies for exciton injection into semiconductor solar cells. As the proposed candidates are composed of fragments from synthesized molecules, they are likely synthetically accessible.", 
    "mcid": "2024.104", 
    "id": "2256", 
    "license": "Creative Commons Attribution 4.0 International", 
    "license_addendum": null, 
    "references": [
      {
        "citation": "L. Schaufelberger J. T. Blaskovits, R. Laplaza, C. Corminboeuf, K. Jorner, ChemRxiv (2024)", 
        "type": "Preprint"
      }
    ], 
    "doi": "10.24435/materialscloud:yn-vz", 
    "keywords": [
      "singlet fission", 
      "machine learning", 
      "uncertainty quantification", 
      "inverse design", 
      "genetic algorithm"
    ], 
    "contributors": [
      {
        "affiliations": [
          "Ecole polytechnique f\u00e9d\u00e9rale de Lausanne (EPFL), Institute of Chemical Sciences and Engineering, Lausanne, Switzerland, CH-1015"
        ], 
        "familyname": "Schaufelberger", 
        "givennames": "Luca"
      }, 
      {
        "affiliations": [
          "Ecole polytechnique f\u00e9d\u00e9rale de Lausanne (EPFL), Institute of Chemical Sciences and Engineering, Lausanne, Switzerland, CH-1015"
        ], 
        "familyname": "Blaskovits", 
        "givennames": "J. Terence"
      }, 
      {
        "affiliations": [
          "Ecole polytechnique f\u00e9d\u00e9rale de Lausanne (EPFL), Institute of Chemical Sciences and Engineering, Lausanne, Switzerland, CH-1015", 
          "National Center for Competence in Research \u2013 Catalysis (NCCR-Catalysis), Ecole polytechnique f\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne, Switzerland, CH-1015"
        ], 
        "familyname": "Laplaza", 
        "givennames": "Ruben"
      }, 
      {
        "affiliations": [
          "Ecole polytechnique f\u00e9d\u00e9rale de Lausanne (EPFL), Institute of Chemical Sciences and Engineering, Lausanne, Switzerland, CH-1015", 
          "National Center for Competence in Research \u2013 Catalysis (NCCR-Catalysis), Ecole polytechnique f\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne, Switzerland, CH-1015"
        ], 
        "familyname": "Corminboeuf", 
        "email": "clemence.corminboeuf@epfl.ch", 
        "givennames": "Clemence"
      }, 
      {
        "affiliations": [
          "ETH Z\u00fcrich, Institute of Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 1, Z\u00fcrich, Switzerland, CH-8093"
        ], 
        "familyname": "Jorner", 
        "email": "kjell.jorner@chem.ethz.ch", 
        "givennames": "Kjell"
      }
    ], 
    "conceptrecid": "2255", 
    "version": 1, 
    "publication_date": "Jul 04, 2024, 13:58:31", 
    "is_last": true, 
    "status": "published", 
    "_files": [
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        "size": 1983009, 
        "checksum": "md5:fcd6eb4ef724671660b2af83596e5150", 
        "description": "Folder containing reFORMED (cores and substituents) as well as the uncurated fragments.", 
        "key": "1_Fragment_Pool.zip"
      }, 
      {
        "size": 437227858, 
        "checksum": "md5:956a72f6b44a2698cd86b4cb166cce11", 
        "description": "Folder containing the TD-DFT calculations of the external test set.", 
        "key": "2_External_Test_Set.zip"
      }, 
      {
        "size": 10400137, 
        "checksum": "md5:95c09f3eebb57c492553296b30f50bbb", 
        "description": "Folder containing the TD-DFT calculations of the (pruned) top candidates from the ucGA, as shown in Figure 7.", 
        "key": "3_Singlet_Fission_Candidates_pruned_adiabatic.zip"
      }, 
      {
        "size": 7324334, 
        "checksum": "md5:031ef970e2c55bbb2395e645bc72dd75", 
        "description": "Folder containing the TD-DFT calculations of top candidates from the ML screening, as shown in Figure 8.", 
        "key": "4_Screening_Based_on_Structure_Property_Relationships.zip"
      }, 
      {
        "size": 5089, 
        "checksum": "md5:211f5e766c4a1ce4bbe8e1c9f4402394", 
        "description": "Folder containing the xyz files for the diradical analysis.", 
        "key": "5_Diradical.zip"
      }, 
      {
        "size": 121066361, 
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        "description": "CSV file containing the tabulated properties for the FORMED database, including SMILES.", 
        "key": "Data_FORMED.csv"
      }, 
      {
        "size": 695, 
        "checksum": "md5:76264694c3905b28e8f3e1085e8a3abc", 
        "description": "READ_ME", 
        "key": "READ_ME.txt"
      }
    ], 
    "title": "Inverse design of singlet fission materials with uncertainty-controlled genetic optimization"
  }, 
  "id": "2256", 
  "updated": "2024-07-04T11:58:31.617752+00:00", 
  "created": "2024-07-03T16:01:20.421974+00:00", 
  "revision": 9
}