Genetic optimization of homogeneous catalysts

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{
  "metadata": {
    "edited_by": 576, 
    "owner": 643, 
    "description": "We present the NaviCatGA package, a versatile genetic algorithm capable of optimizing molecular catalyst structures using well-suited fitness functions to achieve a set of targeted properties. The flexibility and generality of this tool are demonstrated with two examples: i) Ligand optimization and exploration for Ni-catalyzed aryl-ether cleavage manipulating SMILES and using a fitness function derived from molecular volcano plots, ii) multiobjective (i.e., activity/selectivity) optimization of bipyridine N.N'-dioxide Lewis basic organocatalysts for the asymmetric propargylation of benzaldehyde from 3D molecular fragments. We show that evolutionary optimization, enabled by NaviCatGA, is an efficient way of accelerating catalyst discovery that bypasses combinatorial scaling issues and incorporates compelling chemical constraints.", 
    "keywords": [
      "homogeneous catalysis", 
      "volcano plot", 
      "catalysis", 
      "optimization", 
      "organocatalysis"
    ], 
    "is_last": true, 
    "title": "Genetic optimization of homogeneous catalysts", 
    "status": "published", 
    "license_addendum": null, 
    "doi": "10.24435/materialscloud:fz-sw", 
    "conceptrecid": "1222", 
    "_files": [
      {
        "checksum": "md5:5d6fb80ada4d9630dd0a564159cecab5", 
        "description": "Describes the content of example_1.zip and example_2.zip in more detail.", 
        "key": "README.txt", 
        "size": 2516
      }, 
      {
        "checksum": "md5:c7a611baa648f6c0daccfbdd62a33575", 
        "description": "Contains structures for the GA runs in Example 1, training data for the ML model and code snippets of the GA setup.", 
        "key": "example_1.zip", 
        "size": 190974037
      }, 
      {
        "checksum": "md5:f06c2df72bce0bf23a6379ccad8addf1", 
        "description": "Contains structures for the GA runs in Example 2, training data for the MLR model and code snippets of the GA setup.", 
        "key": "example_2.zip", 
        "size": 1661233
      }
    ], 
    "references": [
      {
        "citation": "R. Laplaza, S. Gallarati and C. Corminboeuf. Genetic Optimization of Homogeneous Catalysts, Submitted", 
        "type": "Journal reference"
      }, 
      {
        "citation": "R. Laplaza, C. Corminboeuf. lcmd-epfl/NaviCatGA: v1.0.0 (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.5786559", 
        "comment": "Software associated with the data and the publication.", 
        "url": "https://zenodo.org/record/5786559#.Yebj5vso9hE", 
        "doi": "10.5281/zenodo.5786559", 
        "type": "Software"
      }
    ], 
    "contributors": [
      {
        "givennames": "Ruben", 
        "affiliations": [
          "\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Laboratory for Computational Molecular Design (LCMD), CH-1015 Lausanne, Switzerland", 
          "National Center for Competence in Research-Catalysis (NCCR-Catalysis), \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, CH-1015 Lausanne, Switzerland"
        ], 
        "familyname": "Laplaza"
      }, 
      {
        "givennames": "Simone", 
        "affiliations": [
          "\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Laboratory for Computational Molecular Design (LCMD), CH-1015 Lausanne, Switzerland"
        ], 
        "familyname": "Gallarati"
      }, 
      {
        "givennames": "Clemence", 
        "affiliations": [
          "\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Laboratory for Computational Molecular Design (LCMD), CH-1015 Lausanne, Switzerland", 
          "National Center for Competence in Research-Catalysis (NCCR-Catalysis), \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, CH-1015 Lausanne, Switzerland"
        ], 
        "familyname": "Corminboeuf", 
        "email": "clemence.corminboeuf@epfl.ch"
      }
    ], 
    "_oai": {
      "id": "oai:materialscloud.org:1223"
    }, 
    "publication_date": "Jan 21, 2022, 09:32:03", 
    "mcid": "2022.9", 
    "version": 1, 
    "id": "1223", 
    "license": "Creative Commons Attribution 4.0 International"
  }, 
  "revision": 5, 
  "created": "2022-01-18T16:21:54.608256+00:00", 
  "id": "1223", 
  "updated": "2022-01-21T08:32:03.088961+00:00"
}