Navigating predictions at nanoscale: a comprehensive study of regression models in magnetic nanoparticle synthesis

Journal of Materials Chemistry B, vol. 12, pp. 12652–12664

Abstract

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.

Authors 4

1. Lukas Glänzer Aachen Institute of Applied Medical Engineering Medical Faculty

   RWTH Aachen University

   Affiliation as printed

   Institute of Applied Medical Engineering, Helmholtz Institute, Medical Faculty, RWTH Aachen University, Germany

   Institute of Applied Medical Engineering, Helmholtz Institute, Medical Faculty, RWTH Aachen University, Germany. slabu@me.rwth-aachen.de

2. Lennart Göpfert Aachen Institute of Applied Medical Engineering Medical Faculty

   RWTH Aachen University

   Affiliation as printed

   Institute of Applied Medical Engineering, Helmholtz Institute, Medical Faculty, RWTH Aachen University, Germany

   Institute of Applied Medical Engineering, Helmholtz Institute, Medical Faculty, RWTH Aachen University, Germany. slabu@me.rwth-aachen.de

3. Thomas Schmitz‐Rode Aachen Institute of Applied Medical Engineering Medical Faculty

   RWTH Aachen University

   Affiliation as printed

   Institute of Applied Medical Engineering, Helmholtz Institute, Medical Faculty, RWTH Aachen University, Germany

   Institute of Applied Medical Engineering, Helmholtz Institute, Medical Faculty, RWTH Aachen University, Germany. slabu@me.rwth-aachen.de

4. Ioana Slabu corresponding Aachen Institute of Applied Medical Engineering Medical Faculty

   RWTH Aachen University

   Affiliation as printed

   Institute of Applied Medical Engineering, Helmholtz Institute, Medical Faculty, RWTH Aachen University, Germany

   Institute of Applied Medical Engineering, Helmholtz Institute, Medical Faculty, RWTH Aachen University, Germany. slabu@me.rwth-aachen.de