Advancing quantum-dot optoelectronics: Simulation and machine learning for performance optimization

Colloidal quantum-dots (QDs) are garnering significant attention as a promising material for next-generation optoelectronic devices due to their tunable emission wavelengths, high photoluminescence quantum yield, and solution-process compatibility and scalability. While QD-based technologies have seen widespread adoption in display applications, expanding their use into broader optoelectronic fields requires a precise understanding of their optical and electrical properties. Accurate modeling and prediction of device behaviors are critical for performance optimization, necessitating a comprehensive approach that integrates advanced computational techniques. This review explores state-of-the-art simulation methods and machine learning (ML)-based predictive models for QD devices. Key processes, including charge transport, exciton dynamics, and light outcoupling, are introduced to provide insights into efficiency and stability improvements. Modified electrical simulations are discussed alongside advanced optical simulations to assess the role of material properties and device architectures in determining performance. Additionally, the integration of ML algorithms has emerged as a powerful tool for accelerating device design, leveraging large datasets to efficiently predict and optimize QD structures, material compositions, and processing conditions. By combining computational simulations with ML-driven approaches, this review aims to establish a comprehensive framework for QD optoelectronic device research. The synergy between theoretical modeling and data-driven optimization is expected to enhance the development of high-performance QD-based technologies, paving the way for applications beyond conventional display systems.