Researchers at Qingdao University of Science and Technology Target Machine Learning (Explicable Machine Learning for Predicting High-efficiency Lignocellulose Pretreatment Solvents Based On Kamlet-taft and Polarity Parameters)

青岛科技大学的研究人员目标机器学习（基于kamlet-taft和极性参数预测高效木质纤维素预处理溶剂的可解释机器学习）

Robotics & Machine Learning Daily News2024，Issue(Jun.14) ：92-93.

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Researchers at Qingdao University of Science and Technology Target Machine Learning (Explicable Machine Learning for Predicting High-efficiency Lignocellulose Pretreatment Solvents Based On Kamlet-taft and Polarity Parameters)

青岛科技大学的研究人员目标机器学习（基于kamlet-taft和极性参数预测高效木质纤维素预处理溶剂的可解释机器学习）

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摘要

一位新闻记者兼机器人与机器学习的工作人员新闻编辑每日新闻-机器学习的新研究是一篇报道的主题。根据NewsRx通讯员从中国青岛发回的消息，研究表明：“结合密度泛函理论(DFT)和机器学习(ML)方法，利用104种深共晶溶剂(DES)的Kamlet-Taft参数和极性值，建立了一个内在关系模型。通过氢键酸度(α)、氢键碱度(β)的协同组合，有望筛选出具有高木质纤维素预处理效率的DES。极化（pi*）和分子极性指数(MPI)。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is the subject of a report. According to news originating from Qingdao, People’s Republic of China, by NewsRx correspondents, research stated, “Incorporating density functional theory (DFT) and machine learning (ML) methodologies, an intrinsic relationship model was developed utilizing the Kamlet-Taft parameters and polarity values of 104 deep eutectic solvents (DES). DES with high lignocellulosic pret reatment efficiency were expected to be screened through the synergistic combination of hydrogen bond acidity (alpha), hydrogen bond basicity (beta), polarization (Pi*) and molecular polarity index (MPI).”

Key words

Qingdao/People’s Republic of China/Asia/Cyborgs/Elements/Emerging Technologies/Gases/Hydrogen/Inorganic Chemicals/Machine Learning/Qingdao University of Science and Technology

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出版年

2024

Robotics & Machine Learning Daily News

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