QMrxn20: Thousands of reactants and transition states for competing E2 and SN2 reactions

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{
  "id": "414", 
  "updated": "2021-01-08T09:42:42.359886+00:00", 
  "metadata": {
    "version": 1, 
    "contributors": [
      {
        "givennames": "Guido Falk", 
        "affiliations": [
          "Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials (MARVEL), Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland"
        ], 
        "familyname": "von Rudorff"
      }, 
      {
        "givennames": "Stefan N.", 
        "affiliations": [
          "Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials (MARVEL), Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland"
        ], 
        "familyname": "Heinen"
      }, 
      {
        "givennames": "Marco", 
        "affiliations": [
          "Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials (MARVEL), Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland"
        ], 
        "familyname": "Bragato"
      }, 
      {
        "givennames": "O. Anatole", 
        "affiliations": [
          "Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials (MARVEL), Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland"
        ], 
        "email": "anatole.vonlilienfeld@unibas.ch", 
        "familyname": "von Lilienfeld"
      }
    ], 
    "title": "QMrxn20: Thousands of reactants and transition states for competing E2 and SN2 reactions", 
    "_oai": {
      "id": "oai:materialscloud.org:414"
    }, 
    "keywords": [
      "reaction", 
      "organic molecules", 
      "chemical space", 
      "activation energies", 
      "conformers", 
      "machine learning", 
      "ERC", 
      "MARVEL", 
      "SNSF"
    ], 
    "publication_date": "Jun 09, 2020, 17:25:36", 
    "_files": [
      {
        "key": "README.txt", 
        "description": "Detailed description of the data set", 
        "checksum": "md5:40f4009435d2b44c8cc580c851f085d2", 
        "size": 2607
      }, 
      {
        "key": "energies.txt.gz", 
        "description": "Energies of individual geometries (transition states, reactants, products)", 
        "checksum": "md5:cee59b56045095b8ce28587c7e5c5520", 
        "size": 4867853
      }, 
      {
        "key": "barriers.txt.gz", 
        "description": "Activation energies", 
        "checksum": "md5:1e6f2ca965d55aa84c33ff6af866ab6f", 
        "size": 470903
      }, 
      {
        "key": "geometries.tgz", 
        "description": "All geometries as XYZ files", 
        "checksum": "md5:f15738cb1b3180bbee9d700d49907e19", 
        "size": 82688207
      }
    ], 
    "references": [
      {
        "doi": "10.1088/2632-2153/aba822", 
        "citation": "G. F. von Rudorff, S. N. Heinen, M. Bragato, O. A. von Lilienfeld, Machine Learning: Science and Technology 1, 045026 (2020).", 
        "type": "Journal reference"
      }, 
      {
        "comment": "Preprint where the data generation is discussed", 
        "citation": "G. F. von Rudorff, S. N. Heinen, M. Bragato, O. A. von Lilienfeld, \tarXiv:2006.00504", 
        "url": "https://arxiv.org/abs/2006.00504", 
        "type": "Preprint"
      }
    ], 
    "description": "For competing E2 and SN2 reactions, we report 4'400 validated transition state geometries and 143'200 reactant complex geometries including conformers obtained at MP2/6-311G(d) and DF-LCCSD/cc-pVTZ//MP2/6-311G(d) level of theory. The data covers the chemical compound space spanned by the substituents NO2, CN, CH3, and NH2 and early halogens (F, Cl, Br) as nucleophiles and leaving groups based on an ethane skeleton. Ready-to-use activation energies are given for the different levels of theory where in some cases relaxation effects have been treated with machine learning surrogate models.", 
    "status": "published", 
    "license": "Materials Cloud non-exclusive license to distribute v1.0", 
    "conceptrecid": "413", 
    "is_last": true, 
    "mcid": "2020.55", 
    "edited_by": 111, 
    "id": "414", 
    "owner": 111, 
    "license_addendum": null, 
    "doi": "10.24435/materialscloud:sf-tz"
  }, 
  "revision": 12, 
  "created": "2020-05-31T11:21:48.595191+00:00"
}