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predict-gibbs-energies

Repository associated with https://www.nature.com/articles/s41467-018-06682- 4 used to predict the temperature-dependent Gibbs energies of inorganic crys talline solids.

Also implemented in pymatgen (GibbsComputedStructureEntry) -- https://pymatgen.org/pymatgen.entries.computed_entries.html

Data files

TableS1.csv

440 compounds used for training and testing the SISSO-learned descriptor

masses.json

dictionary of atomic masses (amu) {element : mass}

Gels.json

dictionary of experimental Gibbs energies (chemical potentials) for the elements {temperature (K) : {element : G (eV/atom)}}

Implementing descriptor

PredictG.py

contains class for implementing SISSO-learned descriptor; see comments within and arXiv link for details

POSCAR.mp-1143_Al2O3

structure file for Al2O3 from Materials Project to demonstrate use of descriptor

predict-gibbs-energies

Repository associated with https://www.nature.com/articles/s41467-018-06682-4 used to predict the temperature-dependent Gibbs energies of inorganic crystalline solids.

This repository is mostly static to coincide with the cited paper. For updates since this paper, please see github.com/CJBartel/compmatscipy which contains a similar (and more up-to-date module).

Data files

TableS1.csv

440 compounds used for training and testing the SISSO-learned descriptor

masses.json

dictionary of atomic masses (amu) {element : mass}

Gels.json

dictionary of experimental Gibbs energies (chemical potentials) for the elements {temperature (K) : {element : G (eV/atom)}}

Implementing descriptor

PredictG.py

contains class for implementing SISSO-learned descriptor; see comments within and arXiv link for details

POSCAR.mp-1143_Al2O3

structure file for Al2O3 from Materials Project to demonstrate use of descriptor

1ee15c9228a1ef1473006dd6208581b9579ca848