Predictive optimization of curcumin nanocomposites using hybrid machine learning and physics informed modeling

The aim of the current research was to develop a hybrid computational model that involves the integration of Machine Learning (ML) with Physics-Informed Neural Networks (PINNs) to predict and maximize curcumin nanocomposite performance based on Loading Efficiency (LE%) and Encapsulation Efficiency (EE%). Quantitative experimental design comprising 74 synthesized nanocomposite formulations, selected by power analysis (α = 0.05, power = 0.8) to allow for statistical reliability. Pre-processing of data and model construction was undertaken in Python (v3.11) with libraries such as scikit-learn, TensorFlow, and SHAP (as downloaded from the Python Software Foundation). Different ML regressors were tested, and the highest predictive power was manifested by the Gradient Boosting Regressor (GBR). A custom-defined PINN was constructed to integrate mechanistic understanding from the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory and diffusion-transport constraints. The hybrid model was also elucidated by SHapley Additive exPlanations (SHAP) analysis to find controlling formulation parameters. The optimized integrated model demonstrated extremely good predictive performance (LE%: R² = 0.89, RMSE = 6.24; EE%: R² = 0.87, RMSE = 7.15). Physical constraints enhanced generalization by 23%, confirming robustness of the model. Significant optimization results revealed optimal design parameters with particle diameters ranging from 80 to 200 nm and zeta potentials ranging from - 30 to - 50 mV. The data mining analysis revealed polymer ratio and surfactant concentration to be the most influential variables, consistent with the predictive equation derived in the full text. This hybrid ML-PINN combines the predictive capability of machine learning with the mechanistic interpretability of physics-informed modeling. This is a stable, comprehensible optimization platform for nanocarrier optimization with 40-60% cost reduction in experimental screening. Further research is recommended to validate the framework to other bioactive drugs and for incorporating real-time adaptive optimization for scalable pharmaceutical manufacture.