首页|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)
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)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
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).”
QingdaoPeople’s Republic of ChinaAsiaCyborgsElementsEmerging TechnologiesGasesHydrogenInorganic ChemicalsMachine LearningQingdao University of Science and Technology
NETL
NSTL
