Data-driven discovery of organic electronic materials enabled by hybrid top-down/bottom-up design
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{
"revision": 7,
"id": "1561",
"created": "2022-12-02T14:47:01.636115+00:00",
"metadata": {
"doi": "10.24435/materialscloud:j6-e2",
"status": "published",
"title": "Data-driven discovery of organic electronic materials enabled by hybrid top-down/bottom-up design",
"mcid": "2022.162",
"license_addendum": null,
"_files": [
{
"description": "README file detailing the contents of this record.",
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{
"description": "CSV file containing the tabulated properties for the FORMED database.",
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"size": 99694228,
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{
"description": "CSV file containing the tabulated properties for the selected dimers.",
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"size": 697042,
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{
"description": "CSV file containing the tabulated properties (obtained with ML) for the predicted dimers.",
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"size": 105235515,
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{
"description": "Compressed file with all the XYZ files of the FORMED database.",
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{
"description": "Compressed file with all the XYZ files of the selected dimers.",
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{
"description": "Compressed file with all the XYZ files of the predicted dimers.",
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{
"description": "Chemiscope file containing the properties and structures of the FORMED database.",
"key": "FORMED_chemiscope.json.gz",
"size": 94007012,
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{
"description": "Chemiscope file containing the properties and structures of the selected dimers.",
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"size": 2131293,
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"description": "Chemiscope file containing the properties and structures of the predicted dimers.",
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"size": 824086980,
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{
"description": "Notebook exemplifying how the provided XYZ structures and csv files can be combined to generate the Chemiscope json files.",
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"owner": 643,
"_oai": {
"id": "oai:materialscloud.org:1561"
},
"keywords": [
"organic molecules",
"crystal structures",
"optical properties",
"photophysical properties",
"donor-acceptor copolymers"
],
"conceptrecid": "1560",
"is_last": false,
"references": [
{
"type": "Journal reference",
"comment": "Manuscript to be submitted. Reference will be updated shortly.",
"citation": "J. T. Blaskovits, R. Laplaza, S. Vela, C. Corminboeuf, To be submitted (2022)"
}
],
"publication_date": "Dec 05, 2022, 16:41:42",
"license": "Creative Commons Attribution 4.0 International",
"id": "1561",
"description": "The high-throughput molecular exploration and screening of organic electronic materials often starts with either a 'top-down' mining of existing repositories, or the 'bottom-up' assembly of fragments based on predetermined rules and known synthetic templates. In both instances, the datasets used are often produced on a case-by-case basis, and require the high-quality computation of electronic properties and extensive user input: curation in the top-down approach, and the construction of a fragment library and introduction of rules for linking them in the bottom-up approach. Both approaches are time-consuming and require significant computational resources. Here, we generate a top-down set named FORMED consisting of 117K synthesized molecules containing their optimized structures, associated electronic and topological properties and chemical composition, and use these structures as a vast library of molecular building blocks for bottom-up fragment-based materials design. A tool is developed to automate the coupling of these building block units based on their available Csp2-H bonds, thus providing a fundamental link between the two philosophies of dataset construction. Statistical models are trained on this dataset and a subset of the resulting hybrid top-down/bottom-up compounds (selected dimers), which enable on-the-fly prediction of key ground state (frontier molecular orbital gaps) and excited state (S1 and T1 energies) properties from molecular geometries with high accuracy across all known p-block organic compound space.\nWith access to ab initio-quality optical properties in hand, it is possible to apply this bottom-up pipeline using existing compounds as molecular building blocks to any materials design campaign. To illustrate this, we construct and screen over a million molecular candidates (predicted dimers) for efficient intramolecular singlet fission, the leading candidates of which provide insight into the structural features that may promote this multiexciton-generating process.",
"version": 1,
"contributors": [
{
"affiliations": [
"Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), 1015 Lausanne, Switzerland"
],
"familyname": "Blaskovits",
"givennames": "J. Terence"
},
{
"affiliations": [
"Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), 1015 Lausanne, Switzerland"
],
"familyname": "Laplaza",
"givennames": "R."
},
{
"affiliations": [
"Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), 1015 Lausanne, Switzerland"
],
"familyname": "Vela",
"givennames": "S."
},
{
"email": "clemence.corminboeuf@epfl.ch",
"affiliations": [
"Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), 1015 Lausanne, Switzerland"
],
"familyname": "Corminboeuf",
"givennames": "C."
}
],
"edited_by": 576
},
"updated": "2023-03-22T15:32:00.403547+00:00"
}