Advances in machine learning applications for thiolate‐protected metal nanoclusters with potential metallic core exploration

Abstract Thiolate‐protected metal nanoclusters (TPMNCs) exhibit tunable physicochemical properties governed by quantum effects related to size, composition, assembly, and surface ligands. Atomically precise synthesis enables researchers to directly correlate nanostructure with material performance. However, slight variations in structure can lead to significant and nonlinear quantum effects, making macroscopic properties unpredictable. Therefore, nanolevel property tuning remained challenging before the advanced development of machine learning (ML). In contrast to traditional nanodesign methods, algorithm development based on data enables ML approaches to capture the nonlinear behaviors and electronic features of nanoclusters by embedding characteristics into a high‐dimensional numerical space, thereby improving the predictability and generative capability for property prediction. TPMNCs are a representative system with atomic precision and distinct optical and catalytic properties. This review explores ML applications in nanocluster research, with a focus on TPMNCs, including synthesis, structure prediction, optical property analysis, and catalytic mechanism discovery. The rapid advancement of ML is propelling progress in this field and paving the way for future directions including active learning, model transferability, autonomous experimentation, and adaptive simulation.