Design rules applied to silver nanoparticles synthesis: A practical example of machine learning application

Abstract

The synthesis of silver nanoparticles with controlled physicochemical properties is essential for governing their intended functionalities and safety profiles. However, synthesis process involves multiple parameters that could influence the resulting properties. This challenge could be addressed with the development of predictive models that forecast endpoints based on key synthesis parameters. In this study, we manually extracted synthesis-related data from the literature and leveraged various machine learning algorithms. Data extraction included parameters such as reactant concentrations, experimental conditions, as well as physicochemical properties. The antibacterial efficiencies and toxicological profiles of the synthesized nanoparticles were also extracted. In a second step, based on data completeness, we employed regression algorithms to establish relationships between synthesis parameters and desired endpoints and to build predictive models. The models for core size and antibacterial efficiency were trained and validated using a cross-validation approach. Finally, the features' impact was evaluated via Shapley values to provide insights into the contribution of features to the predictions. Factors such as synthesis duration, scale of synthesis and the choice of capping agents emerged as the most significant predictors. This study demonstrated the potential of machine learning to aid in the rational design of synthesis process and paves the way for the safe-by-design principles development by providing insights into the optimization of the synthesis process to achieve the desired properties. Finally, this study provides a valuable dataset compiled from literature sources with significant time and effort from multiple researchers. Access to such datasets notably aids computational advances in the field of nanotechnology.

Keywords: Safe and sustainable; Shapley values; Silver nanoparticles, synthesis, machine learning.

Graphical abstract

Fig. 1

Residuals plot (left) and prediction errors plot (right) for random forest model. Train set: 1479 rows, test set: 634 rows. The figure represents one instance of the ten fold validation process.

Fig. 2

Top 10 variables impact on predicted core size values (mean absolute SHAP value).

Fig. 3

Residuals plot (left) and prediction errors plot (right) for lightgbm regression model. Train set: 875 rows, test set: 376 rows.

Fig. 4

Variable impact on predicted bacterial reduction (mean absolute SHAP value).