Datasets for machine learning model for efficient non-thermal tuning of the charge density wave in monolayer NbSe2

1H_NbSe2_frames_tot_ML_all.xyz : structures used for training of ML models xdos_0.01_fermi_tot_all.npy : energy grid used for training of KRR model ldos_0.01_fermi_tot_all.npy : constructed eDOS used for training of KRR model 1H_NbSe2_model_6_run-123.model : MACE model without SWA 1H_NbSe2_model_6_run-123_swa.model : MACE model with SWA 86_fermi.json : KRR model, "ML used" model from the main paper 86_fermi-krr-weights.npy : weights of the KRR model c2db-12553.json : unit cell of a structure used for the phonon calculations; downloaded from: https://next-gen.materialsproject.org/ conda_environment.yml : Conda environment used to install librascal version we have used, one should download it from this link: https://github.com/cbenmahm/librascal/tree/finite_T_calc , after that one should replace asemd.py file with the asemd.py file given here; example of a path where asemd.py file is located in the conda environment: anaconda3/envs/rascal_calc/lib/python3.9/site-packages/rascal/models 1H_NbSe2_SSCHA.py : Python script used for SSCHA calculations without electronic temperature 1H_NbSe2_laser.py : Python script used for SSCHA calculations with a finite electronic temperature