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.

MatLab, 2023b

DFTB+, 20.2.1