DFT-guided engineering of halide perovskite quantum dots: From excited-state dynamics and defect chemistry to predictive materials discovery

Halide perovskite quantum dots (HPQDs) have emerged as promising materials for next-generation optoelectronic technologies, yet achieving simultaneously high efficiency, stability, and controllable functionality requires a deeper understanding of their atomic-scale mechanisms. This review highlights how density functional theory (DFT) and its advanced extensions have transformed the rational engineering of HPQDs from empirical optimization toward mechanism-driven design. Time-dependent DFT and nonadiabatic molecular dynamics approaches are discussed for elucidating excited-state evolution, carrier relaxation, charge separation, recombination dynamics, and electron–phonon interactions. The role of DFT in revealing defect chemistry, vacancy-dependent trap formation, dopant stability, and composition-driven band engineering is examined, emphasizing the importance of surface environments and dynamic structural effects in nanoscale systems. Recent advances in DFT-guided surface passivation, heterostructure engineering, and molecular interface design are reviewed through their demonstrated impact on optical performance and device stability. Remaining challenges, including model–experiment discrepancies, computational scaling, and accurate treatment of dynamic surfaces, is analyzed. Finally, emerging strategies integrating machine-learning potentials, high-throughput screening, and atomic-resolution characterization are discussed as pathways toward predictive materials discovery and accelerated development of advanced HPQD-based optoelectronic devices.