Repositori institucional URV
| Author: | Çetin, Yarkin Aybars |
| Published by (editorial): | Universitat Rovira i Virgili (URV) |
| Document type: | info:eu-repo/semantics/other |
| Abstract: | This dataset houses simulation data for “SCC-DFTB/MD simulation: guanine-TiO2 adsorption model.” This set of simulations is a continuation of the set used in the related publication (https://doi.org/10.1016/j.csbr.2024.100008) and mentioned in its dataset (https://doi.org/10.34810/data1223). This dataset shares Self Consistent Charge Density Functional Tight Binding Molecular Dynamics (SCC-DFTB/MD) simulation data of horizontally oriented guanine molecule adsorption on an Anatase-(101), (96 TiO2 6 Trilayers) Slab. The initial oxygen-deficient geometry (Initial_Defective_Model) and stoichiometric geometry (Initial_Stoichiometric_Model) computational chemistry models were obtained as described in the related publication (https://doi.org/10.1016/j.csbr.2024.100008). Afterward, the plane of the guanine molecule was manipulated by rotating the molecule 90 degrees so that it became parallel to the TiO2 surface. In this resulting geometry, an additional distance of 0.5 Angstrom was added between the guanine molecule and the slab surface. Moreover, an alternative starting geometry (Initial_Defective_Oriented_Model) was created for the oxygen-deficient chemistry model, and the oxygen atom in the guanine was ensured to fall exactly on the oxygen vacancy center by adjusting the guanine molecule horizontally. Computational details were implemented as described in a related publication. The prepared molecular models were subjected to MD calculations of at least 16000 steps (16ps (dt=1fs)). The MD simulation was energetically equilibrated after roughly 6000 steps. As in the related publication, the database for every trajectory step’s density of states (DOS) and geometry (GEO) was created through MD simulation. It was used to create Neural Network (NN) replicas via the Machine Learning (ML) technique. ML only considered energetically equilibrated steps. In the alternative starting geometry case, the calculation continued until the 46000th step. In this case, the MD trajectory is considered in two approaches; ML is implemented with equilibrated trajectory steps from 6000 to 16000, from 6000 to the 36000th, and from 6000 to the 46000th step to generate NNs. Please see the “README.txt” file for further details related to dataset organization. |
| Dataset publication year: | 2024 |
| Researcher identifier: | 0000-0003-2456-5949 |
| Related publications: | Prediction of Electronic Density of States in Guanine-TiO2 Adsorption Model based on Machine Learning |
| Keywords: | Molecular Dynamics; Computational Chemistry; Titania; Guanine; Machine Learning |
| Language: | en |
| Departament: | Enginyeria Informàtica i Matemàtiques |
| Related publication's DOI: | 10.1016/j.csbr.2024.100008 |
| Subject matter: | Chemistry |
| Access rights: | info:eu-repo/semantics/openAccess |
| DOI: | 10.34810/data1312 |
| Dataset title: | SCC-DFTB/MD simulation: guanine-TiO2 adsorption model |
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| DocumentPrincipal_txt | DocumentPrincipal_txt | text/plain |
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