A new study introduced a machine learning-powered clustering model that incorporates both basic features and target properties, successfully grouping over 1,000 inorganic materials.

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Following this, the basic features were transformed into z-vectors—information based on the paths taken by the RF model. And finally, cluster analysis was performed on the transformed z-vectors.