Design of efficient blue phosphorescent bottom emitting light emitting diodes by machine learning approach

Machine Learning for Understanding the Relationship between the Charge Transport Mobility and Electronic Energy Levels for n‐Type Organic Field‐Effect Transistors

“…Nevertheless, Random Forest model has been extensively investigated as an ensemble of Regression Tree due to the multiple Regression Tree models generated and used as base models. For instance, Random Forest algorithm can be used to predict the performance of blue phosphorescent bottom‐emitting light‐emitting diodes from various input parameters (e.g., material frontier molecular orbital energy levels, material triplet energies, device structures, and layer thicknesses), which is similar to the condition in this study . Furthermore, Random Forest model has been applied to extract underlying complex correlations in a variety of directions, such as prediction of photovoltaic energy production, identification of optimal Pb‐based perovskites materials, and prediction of organic solar cells properties …”

“…Machine‐learning approaches are widely adopted in materials discovery and are more recently being applied in the domains of design of efficient blue phosphorescent bottom‐emitting light‐emitting diodes, understanding compatibility of organic−inorganic hybrid perovskites with post‐treatment amines, efficiency prediction of organic solar cells, etc. In this work, the machine learning was built for predicting the electron mobility from experimentally available data, thereby identifying the relationship between the electronic properties of n‐type semiconductors (e.g., HOMO and LUMO) and transporting properties of OFETs.…”

“…The R 2 of Random Forest on the cross-validation mean score is 0.63, and on the test set is 0.61, which is acceptable in mobility values prediction. [25] Furthermore, Random Forest model has been applied to extract underlying complex correlations in a variety of directions, such as prediction of photovoltaic energy production, identification of optimal Pb-based perovskites materials, [26] and prediction of organic solar cells properties. The Gradient Boosting model has cross-validation mean score of 0.58 on the train set, and 0.55 on the test set.…”

Development Status and Prospects of Artificial Intelligence in the Field of Energy Conversion Materials

“…They can only rely on manual classification and summary to screen materials with required characteristics, which consumes a lot of labor and time. Applying AI approaches increase the efficiency via modeling and optimization without increasing the cost (Zalesny, 2017;Bin Janai et al, 2018;Kaneko et al, 2019). Hence, AI technology plays a significant role in energy conversion and other fields.…”

Pitfalls of simulation-based machine learning in optoelectronic device design

“…12,13 Light-emitting diodes (LEDs) in particular are a common target of machine learning. 14,15,16 In the following, we evaluate a few of these machine-learning applications that are focused on GaN-based LEDs. These blue light emitters have been receiving great attention in recent years due to their widespread utilization in lighting, displays and other fields.…”