Machine Learning‐Integrated Numerical Simulation for Predicting Photothermal Conversion Performance of Metallic Nanofluids

Abstract Photothermal conversion in metallic nanofluids, driven by localized surface plasmon resonances, is essential for applications in biomedicine, such as cancer treatment and biosensing. However, accurately predicting photothermal conversion performance, particularly the spatial temperature distribution, remains challenging due to the complex interplay of nanoparticle properties. Existing experimental methods are labor‐intensive and often insufficient in providing detailed thermal profiles. Here, a novel approach that integrates machine learning is presented with numerical simulations to predict the photothermal conversion efficiency and spatial temperature distribution in gold nanorod nanofluid. The method employs Discrete Dipole Approximation for optical property calculations, Monte Carlo simulations for light transport, and finite element methods for temperature distribution modeling. The machine learning model, trained on 1,024 cases of photothermal conversion efficiency and 2,016 cases of temperature fields, achieves rapid and accurate predictions with a high correlation coefficient ( R 2 = 0.972) to simulation results. This approach not only streamlines the prediction process but also provides an accessible tool for optimizing nanoparticle design, with significant implications for advancing biomedicine, energy, and sensor technologies.