The Science of the total environment. 2024 Jan 15:908:168168. doi: 10.1016/j.scitotenv.2023.168168 Q18.02025

Application of interpretable machine learning models to improve the prediction performance of ionic liquids toxicity

解释性机器学习模型在离子液体毒性预测中的应用翻译改进

Dingchao Fan¹, Ke Xue¹, Runqi Zhang¹, Wenguang Zhu¹, Hongru Zhang¹, Jianguang Qi¹, Zhaoyou Zhu¹, Yinglong Wang², Peizhe Cui¹

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作者单位

¹ College of Chemical Engineering, Qingdao University of Science and Technology, 53Zhengzhou Road, Qingdao 266042, People's Republic of China.

² College of Chemical Engineering, Qingdao University of Science and Technology, 53Zhengzhou Road, Qingdao 266042, People's Republic of China. Electronic address: wangyinglong@qust.edu.cn.

DOI: 10.1016/j.scitotenv.2023.168168 PMID: 37918734

摘要 Ai翻译

With the wide application prospect of ionic liquids (ILs) as solvent in the future industry, in order to promote green and sustainable chemical engineering, the toxicity problem of common concern has been systematically modeled. Machine learning has promoted the development of chemical property prediction model with its powerful data processing ability. Two typical ensemble learning models, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), were u... ...点击完成人机验证后继续浏览

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期刊名：Science of the total environment

缩写：SCI TOTAL ENVIRON

ISSN：0048-9697

e-ISSN：1879-1026

IF/分区：8.0/Q1

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Application of interpretable machine learning models to improve the prediction performance of ionic liquids toxicity

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