[2PS-127]
A Deep Learning Approach to Surface Passivation Material Design for High-Efficiency Perovskite Light-Emitting Diodes
발표자이동빈 (광주과학기술원)
연구책임자김호범 (광주과학기술원)
Abstract
Surface passivation is a key strategy to improve the external quantum efficiency (EQE) of perovskite light-emitting diodes (PeLEDs) by mitigating non-radiative recombination and stabilizing perovskite interfaces. However, the vast chemical space and the complex relationship between molecular structures and passivation performance present significant challenges in the rational design of effective surface passivators. Herein, we present a deep reinforcement learning (DRL) framework combined with a pre-trained deep neural network (DNN) utilizing KRFP fingerprints and high-importance chemical descriptors. The DNN model, acting as a reward function in the DRL environment, enables the autonomous exploration and optimization of novel surface passivation molecules. Our approach establishes a robust and data-driven strategy for accelerating the discovery of high-efficiency passivation molecules, paving the way for next-generation, high-performance PeLEDs