Optimizing the thermodynamic behavior prediction path of complex ionic solutions using neural network models

The thermodynamic behavior of complex ionic solutions is significantly affected by multicomponent coupling and nonlinearity, and traditional models have limitations in prediction accuracy and applicability. New research ideas and methods need to be introduced to develop predictive models for thermodynamic behavior. Introducing a neural network modeling path that integrates physical prior constraints in the study of thermodynamic properties, utilizing improved network structures, transfer learning, and sample augmentation methods to further enhance the effective prediction of thermodynamic properties of ionic solutions. Building a multi-scale input mode and lightweight network system further enhances the model's generalization ability and physical rationality. Experiments in various complex systems have shown that this method outperforms traditional thermodynamic modeling in terms of accuracy and generalization ability, with more stable predictive ability and wider applicability. In a certain sense, it provides ideas and inspirations for intelligent modeling of complex systems.