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