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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Detalles Bibliográficos
Autor: Artigas, Albert
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
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oai_identifier_str oai:dnet:cora.rdr____::7a780265645dc7e95babee95cec9fccd
network_acronym_str ES
network_name_str España
repository_id_str
spelling Replication Data for A simplified machine learning workflow for identifying potential singlet fission candidates: benzannulated biphenylenes as a case studyArtigas, AlbertChemistryMachine LearningAprendizaje automáticoAprenentatge automàticHidrocarburs aromàtics policíclicsHidrocarburos aromáticos policíclicosPolycyclic aromatic hydrocarbonsFuncional de densitat, Teoria delAnálisis funcionalhttp://id.loc.gov/authorities/subjects/sh85036851ChemoinformaticsComputational chemistrySinglet fissionDensity functional theoryPhotovoltaicsPolycyclic conjugated hydrocarbonsBiphenyleneThis 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.Este conjunto de datos contiene toda la información necesaria para reproducir los resultados presentados en el manuscrito "Streamlined Machine Learning Protocol for the Discovery of Singlet Fission Materials". Incluye un archivo multi-XYZ con las geometrías optimizadas de todas las estructuras moleculares estudiadas, así como archivos .csv que proporcionan las cadenas SMILES correspondientes. Los valores objetivo se obtuvieron mediante cálculos DFT y TD-DFT realizados con Gaussian 16, y los descriptores moleculares para todos los sistemas se generaron utilizando AQME. Además de las propiedades calculadas, el conjunto de datos contiene los valores predichos por ROBERT, herramienta para automatizar y documentar modelos predictivos en química computacional, facilitando la conexión entre la investigación química y las técnicas modernas de aprendizaje automático. Para apoyar aún más la reproducibilidad y la trazabilidad, el conjunto de datos también incluye un informe en PDF generado por ROBERT, que documentan dos rondas sucesivas de entrenamiento y evaluación del modelo.Aquest conjunt de dades conté tota la informació necessària per reproduir els resultats presentats al manuscrit "Streamlined Machine Learning Protocol for the Discovery of Singlet Fission Materials". Inclou un fitxer multi-XYZ amb les geometries optimitzades de totes les estructures moleculars estudiades, així com fitxers .csv que proporcionen les cadenes SMILES corresponents. Els valors objectiu es van obtenir mitjançant càlculs DFT i TD-DFT realitzats amb Gaussian 16, i els descriptors moleculars per a tots els sistemes es van generar amb AQME. A més de les propietats calculades, el conjunt de dades conté els valors predits per ROBERT, l'eina per automatitzar i documentar models predictius en química computacional, fent més fàcil la connexió entre recerca química i tècniques modernes de machine learning. Per reforçar encara més la reproductibilitat i la traçabilitat, el conjunt de dades també inclou un informe en PDF generats per ROBERT, que documenten dues rondes consecutives d'entrenament i avaluació del model.CORA.Repositori de Dades de RecercaArtigas Ruf, AlbertMT(39)2025info:eu-repo/semantics/datasethttps://doi.org/10.34810/DATA2473reponame:CORA.Repositori de Dades de Recercainstname:Consorci de Serveis Universitaris de Catalunya (CSUC)Inglésinfo:eu-repo/semantics/openAccessCC BY 4.0oai:dnet:cora.rdr____::7a780265645dc7e95babee95cec9fccd2026-06-17T12:20:17Z
dc.title.none.fl_str_mv Replication Data for A simplified machine learning workflow for identifying potential singlet fission candidates: benzannulated biphenylenes as a case study
title Replication Data for A simplified machine learning workflow for identifying potential singlet fission candidates: benzannulated biphenylenes as a case study
spellingShingle Replication Data for A simplified machine learning workflow for identifying potential singlet fission candidates: benzannulated biphenylenes as a case study
Artigas, Albert
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
title_short Replication Data for A simplified machine learning workflow for identifying potential singlet fission candidates: benzannulated biphenylenes as a case study
title_full Replication Data for A simplified machine learning workflow for identifying potential singlet fission candidates: benzannulated biphenylenes as a case study
title_fullStr Replication Data for A simplified machine learning workflow for identifying potential singlet fission candidates: benzannulated biphenylenes as a case study
title_full_unstemmed Replication Data for A simplified machine learning workflow for identifying potential singlet fission candidates: benzannulated biphenylenes as a case study
title_sort Replication Data for A simplified machine learning workflow for identifying potential singlet fission candidates: benzannulated biphenylenes as a case study
dc.creator.none.fl_str_mv Artigas, Albert
author Artigas, Albert
author_facet Artigas, Albert
author_role author
dc.contributor.none.fl_str_mv Artigas Ruf, Albert
MT(39)
dc.subject.none.fl_str_mv 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
topic 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
description 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.
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/dataset
format dataset
dc.identifier.none.fl_str_mv https://doi.org/10.34810/DATA2473
url https://doi.org/10.34810/DATA2473
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
CC BY 4.0
eu_rights_str_mv openAccess
rights_invalid_str_mv CC BY 4.0
dc.publisher.none.fl_str_mv CORA.Repositori de Dades de Recerca
publisher.none.fl_str_mv CORA.Repositori de Dades de Recerca
dc.source.none.fl_str_mv reponame:CORA.Repositori de Dades de Recerca
instname:Consorci de Serveis Universitaris de Catalunya (CSUC)
instname_str Consorci de Serveis Universitaris de Catalunya (CSUC)
reponame_str CORA.Repositori de Dades de Recerca
collection CORA.Repositori de Dades de Recerca
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869404030688559104
score 15.811543

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