Enabling Smarter Nanosystems

Nanotechnology has emerged to be one of the most relevant topics in medical, energy, and environmental sciences transformation. However, the behaviors of nanoparticle complexity are hard to master along with the necessity of improving their production for better features. This work addresses the integration of supervised machine learning (SML) to solve these challenges, focusing on the prediction of nanoparticle properties such as toxicity, stability, and catalytic efficiency. A comprehensive SML framework was developed including models such as random forest, XGBoost, and neural networks. The study showed an exceptional prediction accuracy where the achievement of random forest was about 92.8% of toxic predictions and XGBoost performed better than the one against stability regression testing. The application of SHAP and LIME to the model results serves as explainable artificial intelligence (AI) that can explain how size, surface charge, and chemical composition are part of the significant predictors. Experimental validation showed a good correlation between model predictions and laboratory data, thus confirming dependability. Also, due to the application of digital twins, in-situ modeling and optimization of nanoparticle production is achievable in real time, hence doing away with the necessity for trial-and-error methodologies. With such scarcity of data, this work demonstrates the prospect of AI-driven frameworks transforming nanotechnology by increasing predictability, interpretability, and scalability. Further, the paper further presents some future prospects that involve incorporation of reinforcement learning and establishment of shared data repositories in an attempt to increase the capacity of AI in this industry.