Authors
Beomsoo Chun, Byong Jae Kim, Hyeongjin Kim, Jeong Han Song, Yeongsik Kim, Jaehoon Lim, Jeonghun Kwak
Published in
Reports on progress in physics. Physical Society (Great Britain). Volume 89. Issue 7. Jul 14, 2026. Epub Jul 14, 2026.
Abstract
Colloidal quantum dots (QDs) offer size-tunable optoelectronic properties and solution processability, yet achieving uniformly packed emissive layers remains a bottleneck for high-performance quantum-dot light-emitting diodes (QLEDs). Here, we report a machine learning -guided solvent optimization strategy to produce homogeneous QD films. Five representative solvent parameters are evaluated, and multiple regression models are trained against film uniformity derived from atomic force microscopy. Among them, support vector regression provides the highest predictive accuracy for film homogeneity. Guided by these predictions, we formulate a mixed solvent that closely matches the target profile, yielding superior packing homogeneity, which is confirmed by grazing-incidence small-angle x-ray scattering. QLEDs fabricated with this formulation exhibit an external quantum efficiency of 20.6% and an operational lifetime of 468.5 h at 20 000 cd m-2, surpassing all single-solvent controls. These findings establish packing homogeneity as a decisive factor for device performance and introduce a generalizable and scalable framework for data-driven design of solution-processed devices, holding strong potential for next-generation optoelectronic systems.
PMID:
42444276
Bibliographic data and abstract were imported from PubMed on 14 Jul 2026.
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