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.

中文翻译：

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应用于银纳米粒子合成的设计规则：机器学习应用的实际例子。

具有受控物理化学性质的银纳米粒子的合成对于控制其预期功能和安全性至关重要。然而，合成过程涉及多个可能影响最终性能的参数。这一挑战可以通过开发基于关键综合参数预测终点的预测模型来解决。在本研究中，我们从文献中手动提取与合成相关的数据，并利用各种机器学习算法。数据提取包括反应物浓度、实验条件以及物理化学性质等参数。还提取了合成纳米颗粒的抗菌效率和毒理学特征。第二步，基于数据完整性，我们采用回归算法来建立合成参数和所需终点之间的关系，并建立预测模型。使用交叉验证方法对核心尺寸和抗菌效率模型进行训练和验证。最后，通过 Shapley 值评估特征的影响，以深入了解特征对预测的贡献。合成持续时间、合成规模和封端剂的选择等因素成为最重要的预测因素。这项研究证明了机器学习在帮助合理设计合成过程方面的潜力，并通过提供对合成过程优化以实现所需性能的见解，为安全设计原则的开发铺平了道路。最后，本研究提供了一个有价值的数据集，该数据集是由多个研究人员投入大量时间和精力从文献来源汇编而成的。访问此类数据集尤其有助于纳米技术领域的计算进步。