GitHub - spiningup/Machine-learning-methods-for-materials-science: Evaluate various supervised learning methods to predict cohesive energies of solids (kernel ridge regression is the best) · GitHub

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Evaluate various supervised learning methods to predict cohesive energies of solids (kernel ridge regression is the best)

machine-learning-algorithms material-design regression ridge-regression

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Name		Name	Last commit message	Last commit date
icsd		icsd
tests		tests
vasptools		vasptools
README.md		README.md
analyze_alpha.py		analyze_alpha.py
bandgap_expt.dat		bandgap_expt.dat
binary.py		binary.py
calculate_atoms.py		calculate_atoms.py
get_RS.py		get_RS.py
get_atom_properties.py		get_atom_properties.py
get_coordination.py		get_coordination.py
get_exptgap.py		get_exptgap.py
get_exptvol.py		get_exptvol.py
madelung.py		madelung.py
main.py		main.py
ml.py		ml.py
plot_data_statistics.py		plot_data_statistics.py
read_json.py		read_json.py
setup_atoms.py		setup_atoms.py
split_dataset.py		split_dataset.py
visualize.py		visualize.py
write_csv.py		write_csv.py

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Evaluate various supervised learning methods to predict cohesive energies of solids (kernel ridge regression is the best)

machine-learning-algorithms material-design regression ridge-regression

11 stars

1 watching

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