Table 5 Material properties sharing overlap with word embedding feature importances. Machine learning models trained with elemental word embeddings and materials properties are compared to the models trained with MOF composition word embeddings and MOF target properties for the CoREMOF dataset. The feature importances of each model are analyzed, and compared by Jaccard similarity. The top three materials properties most similar to the model trained to MOF target properties are listed.
Target property | 1 | 2 | 3 |
|---|---|---|---|
log(K\(_{H}\)) CO\(_{2}\) | Electronegativity | Poisson’s ratio | Mendeleev’s number |
log(K\(_{H}\)) CH\(_{4}\) | Electronegativity | Poisson’s ratio | Thermal conductivity |
5.8 bar CH\(_{4}\) | Thermal conductivity | Poisson’s ratio | Brinell’s hardness |
65 bar CH\(_{4}\) | Thermal conductivity | Electronegativity | Melting point |
