Replication Data for A simplified machine learning workflow for identifying potential singlet fission candidates: benzannulated biphenylenes as a case study
This dataset contains all the necessary information to reproduce the results presented in the manuscript "Streamlined Machine Learning Protocol for the Discovery of Singlet Fission Materials". It includes a multi-XYZ file with the optimized geometries of all the molecular structures studie...
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| Tipo de recurso: | conjunto de datos |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Consorci de Serveis Universitaris de Catalunya (CSUC) |
| Repositorio: | CORA.Repositori de Dades de Recerca |
| OAI Identifier: | oai:dnet:cora.rdr____::7a780265645dc7e95babee95cec9fccd |
| Acceso en línea: | https://doi.org/10.34810/DATA2473 |
| Access Level: | acceso abierto |
| Palabra clave: | Chemistry Machine Learning Aprendizaje automático Aprenentatge automàtic Hidrocarburs aromàtics policíclics Hidrocarburos aromáticos policíclicos Polycyclic aromatic hydrocarbons Funcional de densitat, Teoria del Análisis funcional http://id.loc.gov/authorities/subjects/sh85036851 Chemoinformatics Computational chemistry Singlet fission Density functional theory Photovoltaics Polycyclic conjugated hydrocarbons Biphenylene |
| Sumario: | This dataset contains all the necessary information to reproduce the results presented in the manuscript "Streamlined Machine Learning Protocol for the Discovery of Singlet Fission Materials". It includes a multi-XYZ file with the optimized geometries of all the molecular structures studied, as well as accompanying .csv files that provide the corresponding SMILES strings. The target values were obtained through DFT and TD-DFT calculations performed with Gaussian 16, and molecular descriptors for all systems were generated using AQME. In addition to the computed properties, the dataset contains the predicted values produced by ROBERT, a tool for automating and documenting predictive models in computational chemistry, making it easier to bridge chemical research with modern machine learning techniques. To further support reproducibility and traceability, the dataset also include a PDF report generated by ROBERT, which document two successive rounds of model training and evaluation. |
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