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

• المؤلفون الرئيسيون: Goh, Kai Leong, Goto, Atsushi, Lu, Yunpeng
• مؤلفون آخرون: School of Physical and Mathematical Sciences
• التنسيق: مقال
• اللغة: English
• منشور في: 2022
• الموضوعات: Science::Chemistry; Polymers; Algorithms
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/161482

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...