<?xml version='1.0' encoding='utf-8'?> <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"> <dc:creator>Blaskovits, J. Terence</dc:creator> <dc:creator>Laplaza, R.</dc:creator> <dc:creator>Vela, S.</dc:creator> <dc:creator>Corminboeuf, C.</dc:creator> <dc:date>2022-12-05</dc:date> <dc: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. With 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.</dc:description> <dc:identifier>https://archive.materialscloud.org/record/2022.162</dc:identifier> <dc:identifier>doi:10.24435/materialscloud:j6-e2</dc:identifier> <dc:identifier>mcid:2022.162</dc:identifier> <dc:identifier>oai:materialscloud.org:1561</dc:identifier> <dc:language>en</dc:language> <dc:publisher>Materials Cloud</dc:publisher> <dc:rights>info:eu-repo/semantics/openAccess</dc:rights> <dc:rights>Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights> <dc:subject>organic molecules</dc:subject> <dc:subject>crystal structures</dc:subject> <dc:subject>optical properties</dc:subject> <dc:subject>photophysical properties</dc:subject> <dc:subject>donor-acceptor copolymers</dc:subject> <dc:title>Data-driven discovery of organic electronic materials enabled by hybrid top-down/bottom-up design</dc:title> <dc:type>Dataset</dc:type> </oai_dc:dc>