Fig. 5: A systematic comparison between PF-GBT models and TPD-based machine learning models. | npj Computational Materials

Fig. 5: A systematic comparison between PF-GBT models and TPD-based machine learning models.

From: Topological feature engineering for machine learning based halide perovskite materials design

Fig. 5

A Results for the 10-fold cross-validation for OIHP bandgaps from 1346 OIHP database16. Three TPD-based machine learning models (blue bars), including TPD-based kernel ridge regression (TPD-KRR), TPD-based support vector regression (TPD-SVR) and TPD-based gradient boosting tree (TPD-GBT) are used in comparison with persistent function-based models (green bars), including PFC-GBT and PH-GBT. B Results for Leave One Group Out (LOGO) cross-validation test for A-site, B-site and X-site.

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