Glänzer Lukas, Göpfert Lennart, Schmitz-Rode Thomas, Slabu Ioana
Institute of Applied Medical Engineering, Helmholtz Institute, Medical Faculty, RWTH Aachen University, Germany.
J Mater Chem B. 2024 Dec 11;12(48):12652-12664. doi: 10.1039/d4tb02052a.
The applicability of magnetic nanoparticles (MNP) highly depends on their physical properties, especially their size. Synthesizing MNP with a specific size is challenging due to the large number of interdepend parameters during the synthesis that control their properties. In general, synthesis control cannot be described by white box approaches (empirical, simulation or physics based). To handle synthesis control, this study presents machine learning based approaches for predicting the size of MNP during their synthesis. A dataset comprising 17 synthesis parameters and the corresponding MNP sizes were analyzed. Eight regression algorithms (ridge, lasso, elastic net, decision trees, random forest, gradient boosting, support vectors and multilayer perceptron) were evaluated. The model performance was assessed root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and standard deviation of residuals. Support vector regression (SVR) exhibited the lowest RMSE values of 3.44 and a standard deviation for the residuals of 5.13. SVR demonstrated a favorable balance between accuracy and consistency among these methods. Qualitative factors like adaptability to online learning and robustness against outliers were additionally considered. Altogether, SVR emerged as the most suitable approach to predict MNP sizes due to its ability to continuously learn from new data and resilience to noise, making it well-suited for real-time applications with varying data quality. In this way, a feasible optimization framework for automated and self-regulated MNP synthesis was implemented. Key challenges included the limited dataset size, potential violations of modeling assumptions, and sensitivity to hyperparameters. Strategies like data regularization, correlation analysis, and grid search for model hyperparameters were employed to mitigate these issues.
磁性纳米颗粒（MNP）的适用性高度依赖于其物理性质，尤其是尺寸。由于合成过程中控制其性质的相互依存参数众多，合成具有特定尺寸的MNP具有挑战性。一般来说，合成控制无法用白盒方法（经验、模拟或基于物理的方法）来描述。为了处理合成控制问题，本研究提出了基于机器学习的方法来预测MNP合成过程中的尺寸。分析了一个包含17个合成参数和相应MNP尺寸的数据集。评估了八种回归算法（岭回归、套索回归、弹性网络回归、决策树、随机森林、梯度提升、支持向量回归和多层感知器）。通过均方根误差（RMSE）、平均绝对误差（MAE）、平均绝对百分比误差（MAPE）和残差标准差来评估模型性能。支持向量回归（SVR）的RMSE值最低，为3.44，残差标准差为5.13。在这些方法中，SVR在准确性和一致性之间表现出良好的平衡。此外，还考虑了诸如对在线学习的适应性和对异常值的鲁棒性等定性因素。总体而言，SVR因其能够持续从新数据中学习并抗噪声的能力，成为预测MNP尺寸最合适的方法，非常适合数据质量各异的实时应用。通过这种方式，实现了一个用于自动化和自我调节MNP合成的可行优化框架。关键挑战包括数据集规模有限、可能违反建模假设以及对超参数敏感。采用了数据正则化、相关性分析和模型超参数网格搜索等策略来缓解这些问题。
