Tipus de document: info:eu-repo/semantics/other
DOI: 10.34810/data1237
Publicacions relacionades: Prediction of electronic density of states in guanine-TiO2 adsorption model based on machine learning doi: 10.1016/j.csbr.2024.100008
Grup de recerca: ASCLEPIUS - Smart Technology for Smart Healthcare
Departament: Enginyeria Informàtica i Matemàtiques
Autor: Çetin, Yarkin Aybars; Martorell Masip, Benjamí; Serratosa, Francesc
Data alta repositori: 2025-04-24
Any de publicació de la dataset: 2024
Matèria: Chemistry; Other
Identificador del investigador: 0000-0003-2456-5949; 0000-0002-7759-8042; 0000-0001-6112-5913
DOI de la publicació relacionada: 10.1016/j.csbr.2024.100008
Idioma: en
Publicat per (editora): Universitat Rovira i Virgili (URV)
Drets d'accés: info:eu-repo/semantics/openAccess
Tipus de dades: Experimental data; Textual data
Resum: 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".
Repositori URV