An ANN-Based Feature Optimization for a Distinct Gold Nanoparticle Synthesis

Abstract

Introduction: The properties of gold nanoparticles (AuNPs) are governed by their shape, structure, and intrinsic characteristics. The applications of gold nanoparticles in medical diagnosis and photovoltaics rely on precisely controlled features. However, achieving this precision is expensive, time-consuming, and labor-intensive due to the need for multiple reagents and highly dependent experimental conditions. Methods: We propose an artificial neural network (ANN)–based feature optimization approach for predicting nanoparticle features to facilitate nanoparticle synthesis. First, computationally less expensive machine learning (ML) models such as random forest and decision tree were used to rank input features to reduce computational time and complexity. Second, forward sequential feature selection (SFS) was applied as a greedy procedure that iteratively identifies the best new feature to add to the selected feature set. Results: We collected a large dataset, including reagent concentrations, temperature, SPR peak, and pH, alongside nanoparticle outcomes. The ANN optimization model was used to design a nanoparticle synthesis experiment that provides the best parameters for synthesizing precise nanoparticles, thereby tailoring particle size for various applications. Discussion: We introduce a novel approach that uses ML methods to determine the best feature ordering and applies an ANN-based SFS strategy to predict the optimized size of AuNPs as the desired output. The proposed ANN-SFS model reduces the need for multiple laboratory procedures typically required for optimizing ANN models in nanoparticle synthesis. Conclusion: This ANN approach improves nanoparticle synthesis compared to traditional optimization methods and supports the advancement of nanomaterial development. The proposed ANN model achieves 90.61% accuracy and a minimum mean square error (MMSE) of 9.4% in the predicted outcomes.