LGB-stack: stacked generalization with LightGBM for highly accurate predictions of polymer bandgap

• Main Authors: Goh, Kai Leong, Goto, Atsushi, Lu, Yunpeng
• Other Authors: School of Physical and Mathematical Sciences
• Format: Article
• Language: English
• Published: 2022
• Subjects: Science::Chemistry; Polymers; Algorithms
• Online Access: https://hdl.handle.net/10356/161482
• Institution: Nanyang Technological University

Recently, the Ramprasad group reported a quantitative structure–property relationship (QSPR) model for predicting the Egap values of 4209 polymers, which yielded a test set R2 score of 0.90 and a test set root-mean-square error (RMSE) score of 0.44 at a train/test split ratio of 80/20. In this paper...