A technology has been developed that allows artificial intelligence to inversely determine the process conditions for quantum-dot light-emitting diode (QLED) devices—conditions that previously required extensive trial and error to identify.
When applied to actual devices, the technology roughly doubled efficiency and extended operational lifetime more than 40-fold, raising expectations that it could accelerate the development of next-generation displays.
Seoul National University's College of Engineering announced that a joint research team led by Prof. Jeonghun Kwak of the Department of Electrical and Computer Engineering and Prof. Jaehoon Lim of Sungkyunkwan University's Department of Energy Science has developed an AI-based platform that inversely designs the optimal solvent properties for arranging quantum dots uniformly and densely during the fabrication of QLEDs.
The research was supported by the Ministry of Science and ICT and the National Research Foundation of Korea through the Future Display Leading Technology Program and the Nano & Material Technology Development Program. The findings were published online on July 15 in Reports on Progress in Physics , an internationally renowned physics journal published by the UK's Institute of Physics (IOP).
Quantum-dot LEDs (QLEDs), which use nanometer-scale semiconductor particles called quantum dots as their light-emitting layer, are regarded as a promising technology for next-generation displays. This is because they can be fabricated using a solution process—coating a substrate with quantum dots in liquid form to create a thin film—making them advantageous for low-cost, large-area production.
To achieve high-performance QLEDs, quantum-dot particles must be arranged uniformly and densely within the thin film, much like bricks. The challenge is that the choice of solvent used to form the film in this solution process significantly affects brightness and lifespan. Because it has been difficult to predict how specific solvent conditions influence performance, researchers have largely relied on experience and repeated experimentation to find optimal conditions—a process that consumes considerable time and cost.
To untangle this complex relationship, the research team trained an AI model to learn the connection between the physical properties of solvents and the resulting structure of quantum-dot thin films. They first fabricated quantum-dot films using five representative solvents and quantified how uniformly the surface had formed using atomic force microscopy (AFM)*. The team then trained a machine learning model on solvent property data—vapor pressure, viscosity, density, dielectric constant, and more—alongside the corresponding film morphology data, enabling it to inversely predict the solvent characteristics that would produce the most uniform quantum-dot film.
*Atomic force microscopy (AFM): equipment that scans a sample's surface with a fine probe to measure its height variations and roughness.
While no single solvent possessed all of the optimal properties suggested by the AI, the research team combined multiple solvents to realize the conditions the AI had proposed. This complex combination—one that would have been difficult to discover through repeated experimentation alone—was applied to an actual QLED fabrication process, resulting in roughly double the efficiency and more than a 40-fold increase in operating lifetime compared to devices made with a single conventional solvent.
"This research demonstrates that AI can be used to design display materials and processes on a data-driven basis," said Prof. Kwak. "We expect it can also be applied to the development of various next-generation electronic devices, including OLEDs and solar cells."
□ Introduction to the SNU College of Engineering
Seoul National University (SNU) founded in 1946 is the first national university in South Korea. The College of Engineering at SNU has worked tirelessly to achieve its goal of ‘fostering leaders for global industry and society.’ In 12 departments, 323 internationally recognized full-time professors lead the development of cutting-edge technology in South Korea and serving as a driving force for international development.
Reports on Progress in Physics
Experimental study
Not applicable
Machine-learning-enabled solvent engineering for uniform quantum dot packing in efficient and stable quantum-dot light-emitting diodes
14-Jul-2026
The authors declare no competing interests.