Fig. 6: Performance of multi-classification and binary classification models for MOF property prediction. | npj Computational Materials

Fig. 6: Performance of multi-classification and binary classification models for MOF property prediction.

From: Property-guided inverse design of metal-organic frameworks using quantum natural language processing

Fig. 6: Performance of multi-classification and binary classification models for MOF property prediction.The alternative text for this image may have been generated using AI.

Training history of loss based on a pore volume and b CO2 Henry’s constant dataset. Accuracy bar graphs of c pore volume and d CO2 Henry’s constant datasets. Note that Multi, Bi00, Bi01, Bi10, and Bi11 represent BoW models trained based on the multi-classification dataset, 00-specialized dataset (binary classification dataset divided into classes 00 and 01 + 10 + 11), 01, 10, and 11-specialized datasets, respectively.

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