The polarity of solvents plays a critical role in various research applications,particularly in their solubilities.Polarity is conveniently characterized by the Kamlet-Taft parameters that is,the hydrogen bonding acid...The polarity of solvents plays a critical role in various research applications,particularly in their solubilities.Polarity is conveniently characterized by the Kamlet-Taft parameters that is,the hydrogen bonding acidity(α),the basicity(β),and the polarizability(π^(*)).Obtaining Kamlet-Taft parameters is very important for designer solvents,namely ionic liquids(ILs)and deep eutectic solvents(DESs).However,given the unlimited theoretical number of combinations of ionic pairs in ILs and hydrogen-bond donor/acceptor pairs in DESs,experimental determination of their Kamlet-Taft parameters is impractical.To address this,the present study developed two different machine learning(ML)algorithms to predict Kamlet-Taft parameters for designer solvents using quantum chemically derived input features.The ML models developed in the present study showed accurate predictions with high determination coefficient(R^(2))and low root mean square error(RMSE)values.Further,in the context of present interest in the circular bioeconomy,the relationship between the basicities and acidities of designer solvents and their ability to dissolve lignin and carbon dioxide(CO_(2))is discussed.Our method thus guides the design of effective solvents with optimal Kamlet-Taft parameter values dissolving and converting biomass and CO_(2)into valuable chemicals.展开更多
基金supported by the U.S.Department of Energy,Office of Science,Biological and Environmental Research Program under award#ERKP752,and the DOE Office of Science,Office of Basic Energy Sciences,Division of Chemical Sciences,Geosciences,and Biosciences(CSGB)(Award No.DE-SC0022214FWP 3ERKCG25)This manuscript has been authored by UT-Battelle,LLC,under contract DEAC05-00OR22725 with the US Department of Energy(DOE)。
文摘The polarity of solvents plays a critical role in various research applications,particularly in their solubilities.Polarity is conveniently characterized by the Kamlet-Taft parameters that is,the hydrogen bonding acidity(α),the basicity(β),and the polarizability(π^(*)).Obtaining Kamlet-Taft parameters is very important for designer solvents,namely ionic liquids(ILs)and deep eutectic solvents(DESs).However,given the unlimited theoretical number of combinations of ionic pairs in ILs and hydrogen-bond donor/acceptor pairs in DESs,experimental determination of their Kamlet-Taft parameters is impractical.To address this,the present study developed two different machine learning(ML)algorithms to predict Kamlet-Taft parameters for designer solvents using quantum chemically derived input features.The ML models developed in the present study showed accurate predictions with high determination coefficient(R^(2))and low root mean square error(RMSE)values.Further,in the context of present interest in the circular bioeconomy,the relationship between the basicities and acidities of designer solvents and their ability to dissolve lignin and carbon dioxide(CO_(2))is discussed.Our method thus guides the design of effective solvents with optimal Kamlet-Taft parameter values dissolving and converting biomass and CO_(2)into valuable chemicals.
