Data-driven machine learning optimisation of silver nanoparticle synthesis
- 1Department of Chemical Engineering, University of Bath Bath BA2 7AY UK
Abstract
Precise control of silver nanoparticle (Ag-NP) size is critical for application-specific performance, yet identification of optimal synthesis conditions remains reliant on trial-and-error experimentation. Here, we report a machine learning inverse design framework trained on a combined experimental dataset spanning batch and continuous flow reactor configurations. Five regression algorithms were evaluated as candidate surrogate models, with Gaussian process (GP) regression emerging as the best-performing model (coefficient of determination (R2) = 0.91 and cross-validation score = 0.80). A random sampling strategy was employed to explore the parameter space and identify synthesis conditions capable of achieving user-defined target Ag-NP hydrodynamic diameters. To differentiate between the multiple feasible solutions, a composite candidate scoring function was implemented to rank synthesis conditions according to the error, estimated Ag-NP dispersity, and posterior uncertainty. The framework was validated across 21 inverse design predictions spanning a target size range of 20–70 nm, achieving average size errors of 3.4% and 7.4% for the batch and flow reactors, respectively. Collectively, this work establishes an experimentally validated inverse design framework capable of identifying synthesis conditions for target Ag-NP hydrodynamic diameters via citrate- and tannic acid-mediated reduction of silver nitrate.
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BibTeX
@article{carver2026datadriven,
title = {{Data-driven machine learning optimisation of silver nanoparticle synthesis}},
author = {Joseph Carver and Y. M. John Chew and Semali Perera and Sourav Chatterjee},
journal = {Nanoscale Advances},
year = {2026},
month = sep,
doi = {10.1039/d6na00557h},
url = {https://doi.org/10.1039/d6na00557h},
note = {CC BY 3.0},
}
RIS
TY - JOUR
TI - Data-driven machine learning optimisation of silver nanoparticle synthesis
AU - Joseph Carver
AU - Y. M. John Chew
AU - Semali Perera
AU - Sourav Chatterjee
PY - 2026
DA - 2026/09/21
JO - Nanoscale Advances
DO - 10.1039/d6na00557h
UR - https://doi.org/10.1039/d6na00557h
AB - Precise control of silver nanoparticle (Ag-NP) size is critical for application-specific performance, yet identification of optimal synthesis conditions remains reliant on trial-and-error experimentation. Here, we report a machine learning inverse design framework trained on a combined experimental dataset spanning batch and continuous flow reactor configurations. Five regression algorithms were evaluated as candidate surrogate models, with Gaussian process (GP) regression emerging as the best-performing model (coefficient of determination (R2) = 0.91 and cross-validation score = 0.80). A random sampling strategy was employed to explore the parameter space and identify synthesis conditions capable of achieving user-defined target Ag-NP hydrodynamic diameters. To differentiate between the multiple feasible solutions, a composite candidate scoring function was implemented to rank synthesis conditions according to the error, estimated Ag-NP dispersity, and posterior uncertainty. The framework was validated across 21 inverse design predictions spanning a target size range of 20–70 nm, achieving average size errors of 3.4% and 7.4% for the batch and flow reactors, respectively. Collectively, this work establishes an experimentally validated inverse design framework capable of identifying synthesis conditions for target Ag-NP hydrodynamic diameters via citrate- and tannic acid-mediated reduction of silver nitrate.
SN - 2516-0230
ER -