Repositori institucional URV
| Author: | Çetin, Yarkın Aybars; Martorell Masip, Benjamí; Serratosa, Francesc |
| Funding program action: | NanoInformaTIX: H2020-NMBP-14-2018-814426; Sbd4Nano: H2020-NMBP-TO-IND-2019-862195; ASCLEPIUS: Smart Technology for Smart Healthcare: 2021SGR-00111 |
| Published by (editorial): | Universitat Rovira i Virgili |
| Document type: | info:eu-repo/semantics/other |
| Departament: | Enginyeria Informàtica i Matemàtiques |
| Abstract: | 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. |
| Subject matter: | Chemistry |
| Access rights: | info:eu-repo/semantics/openAccess |
| Dataset publication year: | 2024 |
| DOI: | 10.34810/data1223 |
| Researcher identifier: | 0000-0003-2456-5949; 0000-0002-7759-8042; 0000-0001-6112-5913 |
| Related publications: | Article Submitted for Review |
| Keywords: | molecular dynamics Computational Chemistry Machine Learning Titania Guanine |
| Dataset title: | Replication Data for: Prediction of Electronic Density of States in Guanine-TiO2 Adsorption Model based on Machine Learning |
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| File | Description | Format | |
|---|---|---|---|
| DocumentPrincipal | DocumentPrincipal | application/zip |
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