Screening Environmentally Benign Ionic Liquids for CO2 Absorption Using Representation Uncertainty-Based Machine Learning

Screening ionic liquids (ILs) with low viscosity, low toxicity, and high CO2 absorption using machine learning (ML) models is crucial for mitigating global warming. However, when candidate ILs fall into the extrapolation zone of ML models, predictions may become unreliable, leading to poor decision-...

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Veröffentlicht in:	Environmental science & technology letters 2024-09, Vol.11 (11), p.1193-1199
Hauptverfasser:	Zhong, Shifa, Chen, Yushan, Li, Jibai, Igou, Thomas, Xiong, Anyue, Guan, Jian, Dai, Zhenhua, Cai, Xuanying, Qu, Xintong, Chen, Yongsheng
Format:	Artikel
Sprache:	eng
Schlagworte:	absorption carbon dioxide Data Science decision making environmental science model uncertainty prediction refractive index standard deviation technology toxicity viscosity
Online-Zugang:	Volltext
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