March 25, 2024 Dataset Open Access

Tahmasbi, Hossein; Ramakrishna, Kushal; Lokamani, Mani; Cangi, Attila

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{
  "@type": "Dataset", 
  "datePublished": "2024-03-25", 
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  "sameAs": [
    "https://www.hzdr.de/publications/Publ-38894"
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  "url": "https://rodare.hzdr.de/record/2778", 
  "identifier": "https://doi.org/10.14278/rodare.2778", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "distribution": [
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      "fileFormat": "xz", 
      "contentUrl": "https://rodare.hzdr.de/api/files/1e768204-7d09-4c4b-99c4-f4c3bfe50e22/FeH_data_RODARE.tar.xz", 
      "@type": "DataDownload"
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  "name": "Data publication: Machine Learning-Driven Structure Prediction for Iron Hydrides", 
  "keywords": [], 
  "creator": [
    {
      "name": "Tahmasbi, Hossein", 
      "@type": "Person", 
      "@id": "https://orcid.org/0000-0002-3072-8217"
    }, 
    {
      "name": "Ramakrishna, Kushal", 
      "@type": "Person", 
      "@id": "https://orcid.org/0000-0003-4211-2484"
    }, 
    {
      "name": "Lokamani, Mani", 
      "@type": "Person", 
      "@id": "https://orcid.org/0000-0001-8679-5905"
    }, 
    {
      "name": "Cangi, Attila", 
      "@type": "Person", 
      "@id": "https://orcid.org/0000-0001-9162-262X"
    }
  ], 
  "description": "<p>Here, we provide the training datasets and the resulting neural network potential for exploring the potential energy surfaces of the FeH system using the minima hopping method. Additionally, data for the minima structures identified in this work are included.</p>", 
  "@id": "https://doi.org/10.14278/rodare.2778"
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