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

ABSTRACT: 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

SUBMITTER: Rahdar A 

PROVIDER: S-EPMC12728167 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature