Thermodynamics-Informed Machine Learning of Organic Electrode Material Solubility in Nonaqueous Electrolytes

) exhibit large systematic errors, thereby limiting the overall predictive accuracy based on first-principles thermodynamics alone. To overcome this limitation, we integrated the physically meaningful computed descriptors into our recently developed symbolic regression framework, SyMANTIC, which learns a corrective model for solubility prediction in nonaqueous electrolytes. Despite the modest data set size, the resulting hybrid model shows good predictive performance under cross-validation and on held-out test compounds, illustrating that this framework can recover compact, chemically meaningful structure-property relationships and offers a new path toward rational design of low-solubility OEMs with reduced reliance on trial-and-error experimentation.