Nanosecond MD of battery cathode materials with electron density description

Data Description for the Paper "Nanosecond Molecular Dynamics of Battery Cathode Materials with Electron Density Description"
The data associated with this paper is provided in a compressed .zip folder identified as Data_MD_trajectories.zip. Within this archive, each trajectory is labeled with temperature annotations: T=600, T=500, and T=400. Additionally, each trajectory includes an index that corresponds to various initial structures and Na-ion concentrations.
The Python scripts for the PaINN model are included in the "graphNNP" zip.
The Python scripts for the charge prediction model are included in the "Charge_Density_Model_scripts.zip" 
Four trained force and energy prediction models are available, identified as follows:
1. best_model_Node148Int4Force99.pth
2. best_model_Node148Int3Force99.pth
3. best_model_Node128Int4Force99.pth
4. best_model_Node128Int3Force99.pth
One trained charge model is available and identified as follows:

best_model_Charge_predict.pth
The training datasets utilized for the development of the force and energy prediction models are denoted as follows:
1. MDDatabase_1.db: The original training dataset derived from Density Functional Theory (DFT) data, used to initially train the 4 energy and force prediction models
2. MDDatabase_2: Dataset obtained through the active learning framework detailed in the paper on a small unit size cell (-40 atoms)
3. MDDatabase_3: Another dataset created using the same active learning framework outlined in the paper, on a larger unit cell (-300 atoms)