Simulation-Assisted Deep Learning Techniques for Commercially Applicable OLED Phosphorescent Materials

Phosphorescent light-emitting
materials play a central role in
organic light-emitting diode (OLED) devices. Due to their synthesis
difficulties, unsystematic trial-and-error synthesis is prohibitively
challenging. For this reason, deep learning (DL), which has shown
success in various fields, is being actively applied to materials
discovery. However, one challenge in applying DL to phosphorescent
materials is the limited amount of experimental data set. One way
to circumvent this issue is to apply powerful DL techniques that have
been successfully implemented in several domains. Another solution
would be to use a large amount of data set for pretraining DL models
with simulated properties highly relevant to target properties. In
this work, phosphorescent materials are represented as strings, molecular
graphs, and point clouds, which are employed by language models, two-dimensional
graph, and three-dimensional graph neural networks. In addition, more
than 200 000 molecules with simulated properties highly relevant
to experimental properties are used for pretraining the DL models.
Our work shows high performance in the prediction of five experimental
properties that are importantly considered when commercializing OLED
devices. This means that faster material discovery for OLEDs can be
achieved through DL models that are trained with simulation information
that is highly correlated with experimental properties.