Incorporating plasmonic featurization with machine learning to achieve accurate and bidirectional prediction of nanoparticle size and size distribution

• Published in: Nanoscale horizons Vol. 7; no. 6; pp. 626 - 633
• Main Authors: Tan, Emily Xi, Chen, Yichao, Lee, Yih Hong, Leong, Yong Xiang, Leong, Shi Xuan, Stanley, Chelsea Violita, Pun, Chi Seng, Ling, Xing Yi
• Format: Journal Article
• Language: English
• Published: England Royal Society of Chemistry 31.05.2022
• Subjects: Anisotropy; Domains; Gold; Machine Learning; Mathematical models; Nanomaterials; Nanoparticles; Nanospheres; Nanostructures; Parameters; Plasmonics; Size distribution
• ISSN: 2055-6756; 2055-6764; 2055-6764
• DOI: 10.1039/d2nh00146b

Determination of nanoparticle size and size distribution is important because these key parameters dictate nanomaterials' properties and applications. Yet, it is only accomplishable using low-throughput electron microscopy. Herein, we incorporate plasmonic-domain-driven feature engineering with machine learning (ML) for accurate and bidirectional prediction of both parameters for complete characterization of nanoparticle ensembles. Using gold nanospheres as our model system, our ML approach achieves the lowest prediction errors of 2.3% and ±1.0 nm for ensemble size and size distribution respectively, which is 3-6 times lower than previously reported ML or Mie approaches. Knowledge elicitation from the plasmonic domain and concomitant translation into featurization allow us to mitigate noise and boost data interpretability. This enables us to overcome challenges arising from size anisotropy and small sample size limitations to achieve highly generalizable ML models. We further showcase inverse prediction capabilities, using size and size distribution as inputs to generate spectra with LSPRs that closely match experimental data. This work illustrates a ML-empowered total nanocharacterization strategy that is rapid (<30 s), versatile, and applicable over a wide size range of 200 nm. Schematic of our bidirectional, ML-empowered approach incorporating plasmonic featurization for rapid (<30 s) and accurate determination of the size and size distribution of gold nanosphere (Au NSs) ensembles in real samples.