ArticleScientific data2022
Organic materials repurposing, a data set for theoretical predictions of new applications for existing compounds.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- A multireference excited state database for correlation-aware screening of organic semiconductors in excitonic energy-materials discovery.Journal of materials chemistry. A · 2026Article
- Predicting room temperature phosphorescence of new organic molecules by combining physical models and data-driven methods.Chemical science · 2026Article
- Cost-Effective Multi-Channel MolOrbImage for Machine-Learned Excited-State Properties of Practical Photofunctional Materials.Journal of chemical theory and computation · 2026Article
- Predicting SJournal of chemical theory and computation · 2026Article
- On the Role of Electronic Correlation and State-Specific Environment Polarization in Singlet-Triplet Gap Inversion.Journal of computational chemistry · 2025Article
- High-Throughput Screening of Molecule/Polymer Photocatalysts for the Hydrogen Evolution Reaction.ACS catalysis · 2025Article
- The application of chemical similarity measures in an unconventional modeling framework c-RASAR along with dimensionality reduction techniques to a representative hepatotoxicity dataset.Scientific reports · 2024Article
- From Chemical Drawing to Electronic Properties of Semiconducting Polymers in Bulk: A Tool for Chemical Discovery.Journal of chemical theory and computation · 2024Article
- Automatic Prediction of Peak Optical Absorption Wavelengths in Molecules Using Convolutional Neural Networks.Journal of chemical information and modeling · 2024Article
- Fine-tuning GPT-3 for machine learning electronic and functional properties of organic molecules.Chemical science · 2024Article
- Identification of Unknown Inverted Singlet-Triplet Cores by High-Throughput Virtual Screening.Journal of the American Chemical Society · 2023Article
- Identification via Virtual Screening of Emissive Molecules with a Small Exciton-Vibration Coupling for High Color Purity and Potential Large Exciton Delocalization.The journal of physical chemistry letters · 2023Article
- Electronic, redox, and optical property prediction of organic π-conjugated molecules through a hierarchy of machine learning approaches.Chemical science · 2022Article
- Virtual Screening for Organic Solar Cells and Light Emitting Diodes.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Review
- Organic materials repurposing, a data set for theoretical predictions of new applications for existing compounds.Scientific data · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
We present a data set of 48182 organic semiconductors, constituted of molecules that were prepared with a documented synthetic pathway and are stable in solid state. We based our search on the Cambridge Structural Database, from which we selected semiconductors with a computational funnel procedure. For each entry we provide a set of electronic properties relevant for organic materials research, and the electronic wavefunction for further calculations and/or analyses. This data set has low bias because it was not built from a set of materials designed for organic electronics, and thus it provides an excellent starting point in the search of new applications for known materials, with a great potential for novel physical insight. The data set contains molecules used as benchmarks in many fields of organic materials research, allowing to test the reliability of computational screenings for the desired application, "rediscovering" well-known molecules. This is demonstrated by a series of different applications in the field of organic materials, confirming the potential for the repurposing of known organic molecules.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.