首页|Research on Machine Learning Discussed by a Researcher at South China University of Technology (Utilizing Machine Learning Models with Molecular Fingerprints and Chemical Structures to Predict the Sulfate Radical Rate Constants of Water ...)
Research on Machine Learning Discussed by a Researcher at South China University of Technology (Utilizing Machine Learning Models with Molecular Fingerprints and Chemical Structures to Predict the Sulfate Radical Rate Constants of Water ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on artificial intelligence have been presented. According to news reporting out of Guangzhou, People’s Republic of China, by NewsRx editors, research stated, “Sulfate radicals are increasingly recognized for their potent oxidative capabilities, making them highly effective in degrading persistent organic pollutants (POPs) in aqueous environments. These radicals excel in breaking down complex organic molecules that are resistant to traditional treatment methods, addressing the challenges posed by POPs known for their persistence, bioaccumulation, and potential health impacts.” Financial supporters for this research include National Science Fund of China For Young Scholars; China Postdoctoral Science Foundation; Guangzhou Basic And Applied Basic Research Foundation. The news editors obtained a quote from the research from South China University of Technology: “The complexity of predicting interactions between sulfate radicals and diverse organic contaminants is a notable challenge in advancing water treatment technologies. This study bridges this gap by employing a range of machine learning (ML) models, including random forest (DF), decision tree (DT), support vector machine (SVM), XGBoost (XGB), gradient boosting (GB), and Bayesian ridge regression (BR) models. Predicting performances were evaluated using R2, RMSE, and MAE, with the residual plots presented. Performances varied in their ability to manage complex relationships and large datasets. The SVM model demonstrated the best predictive performance when utilizing the Morgan fingerprint as descriptors, achieving the highest R2 and the lowest MAE value in the test set. The GB model displayed optimal performance when chemical descriptors were utilized as features.”
South China University of TechnologyGuangzhouPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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