Materia: Chemistry
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
Publicaciones relacionadas: 10.1016/j.csbr.2024.100008
Resumen: 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.
Departamento: Enginyeria Informàtica i Matemàtiques
DOI: 10.34810/data1223
Tipo de documento: info:eu-repo/semantics/other
DOI de la publicación relacionada: Çetin, Y. A., Martorell, B., & Serratosa, F. (2024). Prediction of electronic density of states in guanine-TiO2 adsorption model based on machine learning. Computational and Structural Biotechnology Reports, 1, 100008. https://doi.org/10.1016/j.csbr.2024.100008
Fecha alta repositorio: 2024-04-05
Autor: Çetin, Yarkın Aybars; Martorell Masip, Benjamí; Serratosa, Francesc
Palabras clave: Guanine Titania Machine Learning Computational Chemistry molecular dynamics
Año de publicación de la dataset: 2024
Acción del progama de financiación: NanoInformaTIX: H2020-NMBP-14-2018-814426; Sbd4Nano: H2020-NMBP-TO-IND-2019-862195; ASCLEPIUS: Smart Technology for Smart Healthcare: 2021SGR-00111
Título del conjunto de datos: Replication Data for: Prediction of Electronic Density of States in Guanine-TiO2 Adsorption Model based on Machine Learning
Repositori URV