Materia: Chemistry; Other
Derechos de acceso: info:eu-repo/semantics/openAccess
Identificador del investigador: 0000-0003-2456-5949; 0000-0002-7759-8042; 0000-0001-6112-5913
Publicado por (editorial): Universitat Rovira i Virgili (URV)
Idioma: en
Publicaciones relacionadas: Prediction of electronic density of states in guanine-TiO2 adsorption model based on machine learning doi: 10.1016/j.csbr.2024.100008
Resumen: 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".
Tipos de datos: Experimental data; Textual data
Departamento: Enginyeria Informàtica i Matemàtiques
DOI: 10.34810/data1237
Tipo de documento: info:eu-repo/semantics/other
DOI de la publicación relacionada: 10.1016/j.csbr.2024.100008
Fecha alta repositorio: 2025-04-24
Autor: Çetin, Yarkin Aybars; Martorell Masip, Benjamí; Serratosa, Francesc
Palabras clave: Molecular Dynamics; Poster; Computational Chemistry; Titania; Guanine; Machine Learning
Grupo de investigación: ASCLEPIUS - Smart Technology for Smart Healthcare
Año de publicación de la dataset: 2024
Título del conjunto de datos: Machine Learning Prediction for Electronic Density of States in Guanine-TiO2 Adsorption Model
Repositori URV