Machine Learning-Driven Ink Concentration Optimization in Solution-Processed Organic Light-Emitting Diodes for Enhanced Current Efficiency

Optimizing an organic light-emitting diode (OLED) structure for high efficiency typically requires extensive efforts in tuning layer thicknesses and conducting photometric analyses. While computational simulations can shorten the optimization process, applying them to solution-processed OLEDs (s-OLEDs) is challenging since ink concentration affects both the thickness and morphology of films, complicating the simulation substantially. To address this, we employed machine learning (ML), specifically Gaussian Process Regression (GPR), to optimize s-OLED ink concentrations without the need to calibrate material- or thickness-dependent properties typically required in conventional simulations. The GPR model efficiently suggested optimal ink concentrations for the hole transport layer ( c HTL ) and electron transport layer ( c ETL ) to maximize the current efficiency (CE). Using a data set of …