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- (588bw) Development of a Generalized Gradient Boosting Regression Framework for Predicting IL System Properties
In this work, we employ computational and data analysis techniques to predict the thermophysical properties of single, binary solvent-IL systems. We present a computational framework built on a Gradient Boosting Regression (GBR) algorithm, capable of making generalized density and viscosity predictions for a wide range of IL systems, using features derived from COSMO-RS-generated sigma profiles. We demonstrate the framework's capability to predict both density and viscosity for various IL systems and to capture key property relationships, such as temperature and composition dependencies, observed in experimental data and present important insights for the computer aided design of IL systems.
References
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