Replication Data for: Prediction of Electronic Density of States in Guanine-TiO2 Adsorption Model based on Machine Learning

This dataset houses the code and data related to the paper titled "Prediction of Electronic Density of States in Guanine-TiO2 Adsorption Model based on Machine Learning.” “DEFECTED_MODEL_ML” and “STOICHIOMETRIC_MODEL_ML” folders include 10 instances of neural network generations per model, whic...

ver descrição completa

Detalhes bibliográficos
Autores: Çetin, Yarkın Aybars, Martorell Masip, Benjamí, Serratosa, Francesc
Formato: conjunto de datos
Fecha de publicación:2024
País:España
Recursos:Consorci de Serveis Universitaris de Catalunya (CSUC)
Repositorio:CORA.Repositori de Dades de Recerca
OAI Identifier:oai:dnet:cora.rdr____::8b8cb5c432be55c852b639759f3decdc
Acesso em linha:https://doi.org/10.34810/DATA1223
Access Level:acceso abierto
Palavra-chave:Chemistry
Other
molecular dynamics
Computational Chemistry
Machine Learning
Titania
Guanine
id ES_af62ffab221fd39d175334939c05450f
oai_identifier_str oai:dnet:cora.rdr____::8b8cb5c432be55c852b639759f3decdc
network_acronym_str ES
network_name_str España
repository_id_str
spelling Replication Data for: Prediction of Electronic Density of States in Guanine-TiO2 Adsorption Model based on Machine LearningÇetin, Yarkın AybarsMartorell Masip, BenjamíSerratosa, FrancescChemistryOthermolecular dynamicsComputational ChemistryMachine LearningTitaniaGuanineThis dataset houses the code and data related to the paper titled "Prediction of Electronic Density of States in Guanine-TiO2 Adsorption Model based on Machine Learning.” “DEFECTED_MODEL_ML” and “STOICHIOMETRIC_MODEL_ML” folders include 10 instances of neural network generations per model, which are numbered in the same order given in supplementary material Table S1. The “DEFECTED_MODEL_MD” and “STOICHIOMETRIC_MODEL_MD” folders provide crucial files used in our study per each time step (15050 steps) of molecular dynamics simulations. “GEOMETRIC_COORDINATES_IN_FIGURE_2” and “GEOMETRIC_COORDINATES_IN_FIGURE_3” folders provides the crucial files for each represented inset of Figure 2 and Figure 3 in the main text. Thus, one can reproduce our analysis. “MatLab_Scripts” folder provides the scripts that we used for our study. “MATLAB_ML_CVPAR_25PerCent_15Neur_2Layers” is the script for processing database. “Predict_DOS_from_GEO_URV” enables predicting DOS from Geometry. Steps are described in the code. ## Usage In example one can pick a provided figure inset folder, then can add a desired neural network and the “Predict_DOS_from_GEO_URV” script into the same folder location. Thus the predictions in the study can be reproduced. Furthermore the script enables the applications with different geometry models introduced by user.CORA.Repositori de Dades de RecercaÇetin, Yarkin Aybars2024info:eu-repo/semantics/datasethttps://doi.org/10.34810/DATA1223reponame:CORA.Repositori de Dades de Recercainstname:Consorci de Serveis Universitaris de Catalunya (CSUC)Inglésinfo:eu-repo/semantics/openAccessCC BY-NC 4.0oai:dnet:cora.rdr____::8b8cb5c432be55c852b639759f3decdc2026-06-17T12:20:17Z
dc.title.none.fl_str_mv Replication Data for: Prediction of Electronic Density of States in Guanine-TiO2 Adsorption Model based on Machine Learning
title Replication Data for: Prediction of Electronic Density of States in Guanine-TiO2 Adsorption Model based on Machine Learning
spellingShingle Replication Data for: Prediction of Electronic Density of States in Guanine-TiO2 Adsorption Model based on Machine Learning
Çetin, Yarkın Aybars
Chemistry
Other
molecular dynamics
Computational Chemistry
Machine Learning
Titania
Guanine
title_short Replication Data for: Prediction of Electronic Density of States in Guanine-TiO2 Adsorption Model based on Machine Learning
title_full Replication Data for: Prediction of Electronic Density of States in Guanine-TiO2 Adsorption Model based on Machine Learning
title_fullStr Replication Data for: Prediction of Electronic Density of States in Guanine-TiO2 Adsorption Model based on Machine Learning
title_full_unstemmed Replication Data for: Prediction of Electronic Density of States in Guanine-TiO2 Adsorption Model based on Machine Learning
title_sort Replication Data for: Prediction of Electronic Density of States in Guanine-TiO2 Adsorption Model based on Machine Learning
dc.creator.none.fl_str_mv Çetin, Yarkın Aybars
Martorell Masip, Benjamí
Serratosa, Francesc
author Çetin, Yarkın Aybars
author_facet Çetin, Yarkın Aybars
Martorell Masip, Benjamí
Serratosa, Francesc
author_role author
author2 Martorell Masip, Benjamí
Serratosa, Francesc
author2_role author
author
dc.contributor.none.fl_str_mv Çetin, Yarkin Aybars
dc.subject.none.fl_str_mv Chemistry
Other
molecular dynamics
Computational Chemistry
Machine Learning
Titania
Guanine
topic Chemistry
Other
molecular dynamics
Computational Chemistry
Machine Learning
Titania
Guanine
description This dataset houses the code and data related to the paper titled "Prediction of Electronic Density of States in Guanine-TiO2 Adsorption Model based on Machine Learning.” “DEFECTED_MODEL_ML” and “STOICHIOMETRIC_MODEL_ML” folders include 10 instances of neural network generations per model, which are numbered in the same order given in supplementary material Table S1. The “DEFECTED_MODEL_MD” and “STOICHIOMETRIC_MODEL_MD” folders provide crucial files used in our study per each time step (15050 steps) of molecular dynamics simulations. “GEOMETRIC_COORDINATES_IN_FIGURE_2” and “GEOMETRIC_COORDINATES_IN_FIGURE_3” folders provides the crucial files for each represented inset of Figure 2 and Figure 3 in the main text. Thus, one can reproduce our analysis. “MatLab_Scripts” folder provides the scripts that we used for our study. “MATLAB_ML_CVPAR_25PerCent_15Neur_2Layers” is the script for processing database. “Predict_DOS_from_GEO_URV” enables predicting DOS from Geometry. Steps are described in the code. ## Usage In example one can pick a provided figure inset folder, then can add a desired neural network and the “Predict_DOS_from_GEO_URV” script into the same folder location. Thus the predictions in the study can be reproduced. Furthermore the script enables the applications with different geometry models introduced by user.
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/dataset
format dataset
dc.identifier.none.fl_str_mv https://doi.org/10.34810/DATA1223
url https://doi.org/10.34810/DATA1223
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-NC 4.0
eu_rights_str_mv openAccess
rights_invalid_str_mv CC BY-NC 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_ 1869416679468957696
score 15,812429

Registos relacionados