Machine Learning Algorithms for Automated Synthesis of Biocompatible Nanomaterials

This study introduces a novel approach, the “Machine Learning Algorithms for Automated Synthesis of Biocompatible Nanomaterials,” aimed at revolutionizing the synthesis processes in nanotechnology. The proposed method leverages three powerful machine learning algorithms —Random Forest, Bayesian Optimization, and Deep Neural Networks—to optimize reaction conditions and predict the properties of biocompatible nanomaterials. In the pursuit of automated synthesis, the Random Forest algorithm acts as an ensemble learning technique, combining multiple decision trees to make robust predictions. Bayesian Optimization, a sequential model-based optimization approach, efficiently navigates the parameter space to identify optimal synthesis conditions. Deep Neural Networks, capable of capturing intricate relationships within data, are employed to model complex patterns in the synthesis process. To validate the effectiveness of the proposed method, we conducted comprehensive experiments and compared its performance with six traditional methods: Linear Regression, Polynomial Regression, Support Vector Machine, Decision Tree, K-Nearest Neighbors, and Principal Component Regression. Key performance metrics, including Mean Squared Error (MSE), R-squared ($\mathbf{R}^{2}$), and Mean Absolute Error (MAE), were employed for the evaluation. Our results demonstrate that the proposed method consistently outperforms traditional methods across all metrics. It exhibits lower MSE and MAE values and a higher $\mathbf{R}^{2}$ value, indicating superior predictive accuracy