Do Kyung Lee

University of Nevada, Las Vegas

Papers

1

Total Citations

11

H-Index

1

About

Do Kyung Lee is a pioneering researcher at the forefront of materials informatics and autonomous experimentation, with a core focus on accelerating the discovery and optimization of metal halide perovskites (MHPs) for next-generation optoelectronics. His most impactful work, "AI‐Driven Robot Enables Synthesis‐Property Relation Prediction for Metal Halide Perovskites in Humid Atmosphere" (2025, 11 citations), exemplifies his major contribution: integrating artificial intelligence with Materials Acceleration Platforms (MAPs)—self-driving laboratories—to predict synthesis-property relationships under realistic, humid conditions. By replacing traditional trial-and-error methods with autonomous, AI-guided robotic systems, Lee has demonstrated a paradigm shift that promises order-of-magnitude faster materials discovery. His research bridges the gap between computational prediction and experimental validation, directly addressing stability challenges that have long hindered perovskite commercialization. Though early in his career, Lee’s work has already garnered significant attention for its practical impact on sustainable energy materials. His achievements highlight a visionary approach where robotics and machine learning converge to unlock new frontiers in materials science, making him a key figure to watch in the evolution of autonomous laboratories.

Research Focus

Key Achievements

1
H-Index
1
Papers
11
Total Citations
11
Avg Citations/Paper
🏆 Most Cited Paper
AI‐Driven Robot Enables Synthesis‐Property Relation Prediction for Metal Halide Perovskites in Humid Atmosphere
11 citations · 2025
📈 Most Prolific Year: 2025 (1 Papers)
🤝 Key Collaborators: 15
🏛 Institutions: University of Nevada, Las Vegas

Top Papers

  1. 1

Key Collaborators

Contact & Links

Available for collaboration
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