README file for "Seebeck coefficient of ionic conductors fromBayesian regression analysis" by Enrico Drigo, Stefano Baroni and Paolo Pegolo.

The folders are organized as follows:

• CsF -TP_generator.sh: bash script for the generation of the input file of each trajectory. It also generates the files needed for the computation of the molar enthalpies, as described by Debenedetti, and of the Green-Kubo (GK) integrals. -dic_s_tp.npy: a numpy dictionary with the GK and Bayesian results stored for each temperature and pressure. -p0/: -T1200/: -1200K0bar.npy: numpy dictionary with all the fluxes stored from a trajectory of 40ns generated with the input in traj/input.in . -0_5/ -- 20/: directories for the analysis of the Bayesian regression as a function of the lenght of the trajectory: 0.5--20ns. -K121200K0bar.npy: numpy dictionary for the off diagonal Onsager coefficient computed via the GK integral as defined in the manuscript. -KAPPA1200K0bar.npy: numpy dictionary for the heat-heat Onsager coefficient computed via the GK integral as defined in the manuscript. -SIGMA1200K0bar.npy: numpy dictionary for the electric conductivity computed via the GK integral as defined in the manuscript. -400ns/: directory where we stored the Bayesian results needed for Fig. 1 of the manuscript. -diagonal/:directory for the Bayesian regression of diagonal Onsager coefficients. -KCl/: -dic_s_tp.npy: a numpy dictionary with the GK and Bayesian results stored for each temperature and pressure. -KCl-molten-model.pb: DeePMD model for the molten KCl as described in the manuscript. -p0/: -T1500/: -in.lmp: input file for the LAMMPS simulation.

-LiCl/: analogous scripts as for KCl -NaCl/: analogous scripts as for KCl -notebooks/: -seebeck_molten_salts.ipynb: jupiter notebook for the generation of Fig. 2-3-4. -seebeck_distribution.ipynb: jupiter notebook for the generation of Fig. 1. -test_diagonal_coefficients.ipynb: jupiter notebook for the generation of Fig. 5. -sportran/: directory with the Sportran python package for the cepstral analysis and the Bayesian regression analysis of time series for transport properties of materials. -transportwithdensities/: directory with the Python scripts for the computation of the molar enthalpies and GK integrals.