This dataset contains structures, distance descriptors and relative energies of two-polaron configurations in two different defected supercells of Li4Ti5O12. Additionally, it contains jupyter notebooks with XGBoost models used to predict the energies of polarons in each supercell from the given descriptors. Data Structure The main directory contains the following two subdirectories: lto_cell_A: This directory contains two subdirectories, opt_pol_structures and Notebook_ML_Model. opt_pol_structures: It contains directories for the DFT+U optimized geometry files, including occupation matrices for different polaronic configurations for LTO_Cell_A, used for training and testing the machine learning model. Notebook_ML_Model: It contains the following files: all_calculated_3.dat: This file contains the DFT calculated relative energies and features corresponding to different polaronic configurations for LTO_Cell_A. polaron_data: This file contains features for all possible 780 polaronic configurations for the LTO simulation cell A. XGB_reg.ipynb: This Jupyter notebook is used for training and evaluating the XGBoost regression model. It includes data preprocessing, model training, and evaluation steps, as well as visualizations of the results. lto_model.json: This file contains the saved XGBoost model in JSON format. training.dat and test.dat: These files contain the training and testing datasets used for the model. lto_cell_B: This directory also contains two subdirectories, opt_pol_structures and Notebook_ML_Model. opt_pol_structures: Similar to the one in lto_cell_A, it contains directories for the DFT+U optimized geometry files for LTO_Cell_B. Notebook_ML_Model: It contains the following files: all_calculated.dat: This file contains the DFT+U calculated relative energies and features corresponding to different 196 polaronic configurations for LTO_Cell_B used for training and testing purposes. all_feature.dat: This file contains features for all possible 74880 polaronic configurations for the LTO simulation cell B. XGB_reg.ipynb: This Jupyter notebook is used for training and evaluating the XGBoost regression model for LTO_Cell_B. It includes data preprocessing, model training, and evaluation steps, as well as visualizations of the results. lto_model.json: This file contains the saved XGBoost model in JSON format for LTO_Cell_B. training.dat and test.dat: These files contain the training and testing datasets used for the model in LTO_Cell_B. lto_cell_C: This directory also contains two subdirectories, opt_pol_structures and Notebook_ML_Model. opt_pol_structures: Similar to the one in lto_cell_A, it contains directories for the DFT+U optimized geometry files for LTO_Cell_C. Notebook_ML_Model: It contains the following files: all_calculated.dat: This file contains the DFT+U calculated relative energies and features corresponding to different 188 polaronic configurations for LTO_Cell_C used for training and testing purposes. XGB_reg.ipynb: This Jupyter notebook is used for training and evaluating the XGBoost regression model for LTO_Cell_C. It includes data preprocessing, model training, and evaluation steps, as well as visualizations of the results. training.dat and test.dat: These files contain the training and testing datasets used for the model in LTO_Cell_C.