A review of applications of machine learning in quantum dots research

Machine learning (ML) is increasingly applied in quantum dot (QD) research to support data analysis, device control, materials optimization, sensing, and theoretical modeling. This review surveys recent ML-based approaches across experimental, applied, and computational QD studies, with emphasis on how data-driven methods are embedded within established physical workflows rather than treated as standalone solutions. ML techniques are examined in the context of automated device tuning, high-throughput characterization, synthesis parameter exploration, chemical and biological sensing, photonic and optoelectronic device analysis, and reduced-order modeling of interacting quantum systems. In most cases, ML serves to replace time-consuming fitting procedures, guide experimental sampling, or approximate computationally intensive simulations. Common methodological patterns include supervised learning on limited datasets, transfer learning across device instances, and hybrid approaches incorporating physical constraints into model design or training objectives. Recurrent limitations are also identified, including dataset bias, restricted cross-laboratory transferability, lack of standardized benchmarks, and limited treatment of uncertainty. Rather than positioning ML as a standalone solution, this work frames it as a complementary tool whose reliability and scientific value depend on integration with physical insight, experimental design, and validation protocols.