首页|Reports Summarize Machine Learning Findings from Oak Ridge National Laboratory ( High-throughput Screening and Accurate Prediction of Ionic Liquid Viscosities Us ing Interpretable Machine Learning)
Reports Summarize Machine Learning Findings from Oak Ridge National Laboratory ( High-throughput Screening and Accurate Prediction of Ionic Liquid Viscosities Us ing Interpretable Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Machine Learning are discussed in a new report. According to news reporting out of Oak Ridge, Ten nessee, by NewsRx editors, research stated, "Ionic liquids (ILs) are a novel gro up of green solvents with great promise for various industrial applications, inc luding carbon capture and lignocellulosic biomass deconstruction. However, the u se of ILs at the industrial scale remains challenging due to their high viscosit ies at ambient temperatures." Financial supporters for this research include United States Department of Energ y (DOE), United States Department of Energy (DOE), United States Department of E nergy (DOE). Our news journalists obtained a quote from the research from Oak Ridge National Laboratory, "To develop ILs with lower viscosities, a systematic study of their quantitative structure-property relationship (QSPR) is desirable. Here, we devel oped four machine learning (ML) models to predict viscosity at various temperatu re and pressure ranges, trained over a wide range of ILs consisting of various c ationic and anionic families. ML methods including two-factor polynomial regress ion (two-factor PR), support vector regression (SVR), feed-forward neural networ ks (FFNN), and categorical boosting (CATBoost) were developed based on features that have proven useful in previous ML studies: COSMO-RS (conductor-like screeni ng model for real solvents)-derived surface screening charge densities (sigma pr ofiles). FFNN and CATBoost were the most accurate in predicting IL viscosities w ith lower average absolute relative deviation and higher R 2 values on the test set. Tanimoto similarity scores were calculated to characterize the chemical spa ce and structural similarity of the investigated ions. Furthermore, SHapley Addi tive exPlanation (SHAP) analysis was employed to interpret the ML results. Tempe rature, the polar area of ILs, and the nonpolar regions of ions are key features that influence the viscosity predictions."
Oak RidgeTennesseeUnited StatesNor th and Central AmericaCyborgsEmerging TechnologiesMachine LearningOak Ri dge National Laboratory
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