ML powered, automated discovery of polymer membranes for carbon capture

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{
  "revision": 3, 
  "id": "1629", 
  "created": "2023-01-25T13:14:43.178227+00:00", 
  "metadata": {
    "doi": "10.24435/materialscloud:ma-eq", 
    "status": "published", 
    "title": "ML powered, automated discovery of polymer membranes for carbon capture", 
    "mcid": "2023.14", 
    "license_addendum": "CDLA-Sharing-1.0", 
    "_files": [
      {
        "description": "Jupyter notebook with an example of how to use the Polymer Property Prediction Engine with APIs (application programming interface)", 
        "key": "ppf_API_demo.ipynb", 
        "size": 11395, 
        "checksum": "md5:468d276cf957d2636c5876ac76f9f973"
      }, 
      {
        "description": "Input file data-set for the Jupyter notebook", 
        "key": "example.csv", 
        "size": 550, 
        "checksum": "md5:77a1abfca19c1a53bfc954c0372338b6"
      }, 
      {
        "description": "1,169 calculated homo-polymers properties: IUPAC polymer name, OPSIN SMILES, log10( CO2 permeability (Barrer)), glass transition temperature (K) and half decomposition temperature (K)", 
        "key": "PCO2-Tg-Thd-data-all-simulated.csv", 
        "size": 193657, 
        "checksum": "md5:534cf22962db6838cb93cd023a1c2306"
      }, 
      {
        "description": "784 homopolymers generated by Inverse Design and Machine Learning techniques with the OPSIN SMILES, glass transition temperature (Tg), half decomposition temperature (Thf) and CO2 permeability (PCO2). Tg and Thf are calculated using Polymer Property Prediction Engine, and PCO2 was calculated by the Machine Learning regression model.", 
        "key": "results_monomers_generative_model.csv", 
        "size": 83698, 
        "checksum": "md5:50e094dcc18de52b03a67cc6d4e8a6c9"
      }, 
      {
        "description": "Jupyter notebook for the training and regression models with the open-source version of IMD engine which is available at: \r\nhttps://github.com/GT4SD/molgx-core/", 
        "key": "model_molgx.ipynb", 
        "size": 298634, 
        "checksum": "md5:f9b879f150fb863789f943d42b09db66"
      }
    ], 
    "owner": 726, 
    "_oai": {
      "id": "oai:materialscloud.org:1629"
    }, 
    "keywords": [
      "Polymeric membranes", 
      "CO2 separation", 
      "Carbon dioxide separation"
    ], 
    "conceptrecid": "1312", 
    "is_last": false, 
    "references": [
      {
        "type": "Preprint", 
        "doi": "10.48550/arXiv.2206.14634", 
        "url": "https://arxiv.org/abs/2206.14634", 
        "comment": "Preprint where the data is discussed", 
        "citation": "R. Giro et al."
      }
    ], 
    "publication_date": "Jan 25, 2023, 14:57:35", 
    "license": "Materials Cloud non-exclusive license to distribute v1.0", 
    "id": "1629", 
    "description": "Data sets and scripts for computational discovery of polymer membranes for carbon dioxide separation. The training data set with 1,169 homo-polymers provides carbon dioxide permeability, glass transition temperature and half decomposition temperature for each listed material. The output data set contains 784 optimized homo-polymer candidates generated by Inverse Design and Machine Learning techniques. The Jupyter notebook enables the use of the Polymer Property Prediction Engine as a service for generating the properties provided in the training data set.", 
    "version": 6, 
    "contributors": [
      {
        "email": "rgiro@br.ibm.com", 
        "affiliations": [
          "IBM Research Brazil - Avenida Rep\u00fablica do Chile, 330 - 11o. e 12. andares Rio De Janeiro, RJ 20031-170, Brazil"
        ], 
        "familyname": "Giro", 
        "givennames": "Ronaldo"
      }, 
      {
        "affiliations": [
          "IBM Research Tokyo - 7-7 Shinkawasaki Saiwai-Ku Kawasaki, 14 212-0032, Japan"
        ], 
        "familyname": "Hsu", 
        "givennames": "Hsianghan"
      }, 
      {
        "affiliations": [
          "IBM Research Tokyo - 7-7 Shinkawasaki Saiwai-Ku Kawasaki, 14 212-0032, Japan"
        ], 
        "familyname": "Kishimoto", 
        "givennames": "Akihiro"
      }, 
      {
        "affiliations": [
          "IBM Research Tokyo - 7-7 Shinkawasaki Saiwai-Ku Kawasaki, 14 212-0032, Japan"
        ], 
        "familyname": "Hama", 
        "givennames": "Toshiyuki"
      }, 
      {
        "affiliations": [
          "IBM Research Brazil - Avenida Rep\u00fablica do Chile, 330 - 11o. e 12. andares Rio De Janeiro, RJ 20031-170, Brazil"
        ], 
        "familyname": "Neumann", 
        "givennames": "Rodrigo F."
      }, 
      {
        "affiliations": [
          "IBM Research - 1101 Kitchawan Rd PO Box 218 Yorktown Heights, NY 10598-0218, USA"
        ], 
        "familyname": "Luan", 
        "givennames": "Binquan"
      }, 
      {
        "affiliations": [
          "IBM Research Tokyo - 7-7 Shinkawasaki Saiwai-Ku Kawasaki, 14 212-0032, Japan"
        ], 
        "familyname": "Takeda", 
        "givennames": "Seiji"
      }, 
      {
        "affiliations": [
          "IBM Research Tokyo - 7-7 Shinkawasaki Saiwai-Ku Kawasaki, 14 212-0032, Japan"
        ], 
        "familyname": "Hamada", 
        "givennames": "Lisa"
      }, 
      {
        "affiliations": [
          "IBM Research Brazil - Avenida Rep\u00fablica do Chile, 330 - 11o. e 12. andares Rio De Janeiro, RJ 20031-170, Brazil"
        ], 
        "familyname": "B. Steiner", 
        "givennames": "Mathias"
      }
    ], 
    "edited_by": 576
  }, 
  "updated": "2023-01-30T11:44:57.967403+00:00"
}