Data-efficient prediction of OLED optical properties enabled by transfer learning

It has long been desired to enable global structural optimization of organic light-emitting diodes (OLEDs) for maximal light extraction. The most critical obstacles to achieving this goal are time-consuming optical simulations and discrepancies between simulation and experiment. In this work, by leveraging transfer learning, we demonstrate that fast and reliable prediction of OLED optical properties is possible with several times higher data efficiency compared to previously demonstrated surrogate solvers based on artificial neural networks. Once a neural network is trained for a base OLED structure, it can be transferred to predict the properties of modified structures with additional layers with a relatively small number of additional training samples. Moreover, we demonstrate that, with only a few tenths of experimental data sets, a neural network can be trained to accurately predict experimental measurements of OLEDs, which often differ from simulation results due to fabrication and measurement errors. This is enabled by transferring a pre-trained network, built with a large amount of simulated data, to a new network capable of correcting systematic errors in experiment. Our work proposes a practical approach to designing and optimizing OLED structures with a large number of design parameters to achieve high optical efficiency.