P‐122: Bayesian Optimization with Gradients for OLED Efficiency Enhancement

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Abstract

In order to enhance Organic Light‐Emitting Diode (OLED) efficiency, typically a multitude of devices are fabricated to test the many possible permutations of materials and layer thicknesses. Simulation can be used to predict device performance in advance, reducing the trial and error in specifying test cells. For example, a dipole emission model can be used to predict outcoupling efficiency and color characteristics with ease, but predicting device efficiency is more challenging because it depends on both optical cavity effects as well as charge transport. Accurate models for charge/exciton transport with carefully calibrated material parameters are required. In this work, assuming well characterized material parameters, we turn to the task of optimizing the design of an OLED device in order to maximize efficiency. A Bayesian Optimization algorithm that incorporates gradient information is applied to globally maximize OLED efficiency with minimal computational cost. In contrast, conventional optimization approaches, such as the Response Surface Methodology (RSM) and Particle Swarm Optimization (PSO), require a large number of objective function evaluations (with each evaluation demanding one device simulation).

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