Machine Learning Prediction for Electronic Density of States in Guanine-TiO2 Adsorption Model

DOI

This dataset houses a research poster and its poster abstract. The set of documents was first presented at the doctoral days organized by the Doctoral Committee of the Nanoscience, Materials and Chemical Engineering program at Escuela Técnica Superior de Ingeniería Química (ETSEQ) of Universitat Rovira i Virgili (URV) on 16 May 2024 (19th Edition). Poster Title: "Machine Learning Prediction for Electronic Density of States in Guanine-TiO2 Adsorption Model".

DFTB+, 20.2.1

MatLab, 2023b

Identifier
DOI https://doi.org/10.34810/data1237
Related Identifier IsCitedBy https://doi.org/10.1016/j.csbr.2024.100008
Metadata Access https://dataverse.csuc.cat/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.34810/data1237
Provenance
Creator Çetin, Yarkin Aybars ORCID logo; Martorell Masip, Benjamí ORCID logo; Serratosa, Francesc ORCID logo
Publisher CORA.Repositori de Dades de Recerca
Contributor Çetin, Yarkin Aybars; Martorell Masip, Benjamí; Universitat Rovira i Virgili
Publication Year 2024
Funding Reference European Commission H2020-NMBP-14-2018-814426 ; European Commission H2020-NMBP-TO-IND-2019-862195 ; Generalitat de Catalunya (ES) 2021SGR-00111
Rights CC BY-NC 4.0; info:eu-repo/semantics/openAccess; http://creativecommons.org/licenses/by-nc/4.0
OpenAccess true
Contact Çetin, Yarkin Aybars (Universitat Rovira i Virgili); Martorell Masip, Benjamí (Universitat Rovira i Virgili)
Representation
Resource Type Experimental data; Dataset
Format application/pdf; text/plain
Size 33974511; 29253; 5000
Version 2.0
Discipline Chemistry; Natural Sciences
Spatial Coverage Tarragona, Spain.