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