Enhancing ILThermo Reliability for Machine Learning: Statistical Resolution of Conflicted Activity Data

The reliability of thermodynamic modeling, particularly for ionic liquid (IL) and solute systems, can be critically compromised by conflicting activity coefficient data within databases such as ILThermo. These discrepancies, often arising from variations in experimental conditions or inconsistencies in the source, can lead to skewed predictions and hinder accurate analysis. This issue directly impacts the design and optimization of processes involving ionic liquids (ILs), such as separation, extraction, and catalysis, where precise thermodynamic parameters are crucial. To address this, we present a systematic and rigorous methodology for identifying and resolving such data conflicts. Our approach employs the Chow test for structural stability to assess the consistency of regression models, based on the Gibbs-Helmholtz concept, across various subsets of data. The Chow test considers whether the regression coefficients differ significantly, thereby detecting inconsistencies between subsets and identifying potential conflicts. By integrating this test, we ensure that the data is consistent with thermodynamic principles, validating the physical relevance of our findings. This process not only rectifies existing inconsistencies but also enhances the overall quality and reliability of the dataset, paving the way for further analysis and modeling. Consequently, our methodology establishes a proactive framework for the ongoing refinement and supervision of thermodynamic data, essential for the development of more accurate predictive models.