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Abstract
Data-driven methods are receiving increasing attention to accelerate materials design and discovery for organic light-emitting diodes (OLEDs). Machine learning (ML) has enabled high-throughput screening of materials properties to suggest new candidates for organic electronics. However, building reliable predictive ML models requires creating and managing a high volume of data that adequately address the complexity of materials’ chemical space. In this regard, active learning (AL) has emerged as a powerful strategy to efficiently navigate the search space by prioritizing the decision-making process for unexplored data. This approach allows a more systematic mechanism to identify promising candidates by minimizing the number of computations required to explore an extensive materials library with diverse variables and parameters. In this paper, we applied a workflow of AL that accounts for multiple optoelectronic parameters to identify materials candidates for hole-transport layers (HTL) in OLEDs. Results of this work pave the way for efficient screening of materials for organic electronics with superior efficiencies before laborious simulations, synthesis, and device fabrication.
Highlights
Organic light-emitting diodes (OLEDs) have received significant attention as the most demanded forthcoming display and lighting technology because of their low cost, lightweight, low power consumption, high brightness, and high contrast (Chen et al, 2018; Lee et al, 2019a; Lee et al, 2019b; Abroshan et al, 2020a; Abroshan et al, 2020b; Abroshan et al, 2020c; Abroshan et al, 2020d)
We developed an automated workflow to combine active learning (AL) with density functional theory (DFT) calculations to predict the optoelectronic properties of organic light-emitting diodes (OLEDs) materials
We present an active learning workflow for accelerated design and optimization of novel OLED materials
Summary
Organic light-emitting diodes (OLEDs) have received significant attention as the most demanded forthcoming display and lighting technology because of their low cost, lightweight, low power consumption, high brightness, and high contrast (Chen et al, 2018; Lee et al, 2019a; Lee et al, 2019b; Abroshan et al, 2020a; Abroshan et al, 2020b; Abroshan et al, 2020c; Abroshan et al, 2020d). Recent developments in OLEDs with flexible panels have opened a new avenue for innovative technologies to fabricate cost-effective large-area, wearables, foldable and shape-fitting displays (Jeong et al, 2020; Song et al, 2020; Yoo et al, 2020). OLED devices showing high quantum efficiency and long lifetimes are in great demand for display and lighting. These optoelectronic devices are composed of multiple layers of thin films, each presenting a different functionality. The stability and the overall performance of the OLEDs are impacted by the properties of the materials in each of the layers, such as chemical structure, morphology, thermal and chemical stability, energy levels, and charge mobility
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