Machine Learning-Aided Design of Gold Core–Shell Nanocatalysts toward Enhanced and Selective Photooxygenation

We demonstrate the use of the machine learning (ML) tools to rapidly and accurately predict the electric field as a guide for designing core–shell Au–silica nanoparticles to enhance ¹O₂ sensitization and selectivity of organic synthesis. Based on the feature importance analysis, obtained from a deep neural network algorithm, we found a general and linear dependent descriptor (θ ∝ aD⁰.²⁵t⁻¹, where a, D, and t are the shape constant, size of metal nanoparticles, and distance from the metal surface) for the electric field around the core–shell plasmonic nanoparticle. Directed by the new descriptor, we synthesized gold-silica nanoparticles and validated their plasmonic intensity using scanning transmission electron microscopy-electron energy loss spectroscopy (STEM-EELS) mapping. The nanoparticles with θ = 0.40 demonstrate an ∼3-fold increase in the reaction rate of photooxygenation of anthracene and 4% increase in the selectivity of photooxygenation of dihydroartemisinic acid (DHAA), a long-standing goal in organic synthesis. In addition, the combination of ML and experimental investigations shows the synergetic effect of plasmonic enhancement and fluorescence quenching, leading to enhancement for 1O2 generation. Our results from time-dependent density functional theory (TD-DFT) calculations suggest that the presence of an electric field can favor intersystem crossing (ISC) of methylene blue to enhance ¹O₂ generation. The strategy reported here provides a data-driven catalyst preparation method that can significantly reduce experimental cost while paving the way for designing photocatalysts for organic drug synthesis.