Physics-Informed Support Vector Regression for Predicting Copolymer LCST Depressions via Thermodynamic Hofmeister Featurization
This paper presents a physics-informed Support Vector Regression framework utilizing continuous thermodynamic descriptors and strict leave-one-salt-out validation to robustly predict copolymer LCST depressions across diverse electrolyte environments, overcoming the generalization failures of standard data-driven models.