| 研究実績の概要 |
Organic thin films on metallic substrates are widely used as charge transport layers in OLEDs (organic light emitting diodes) and other organic electronics.
In this project, we aimed to create a machine learning algorithm which can find the optimal deposition conditions for creating highly crystalline, small-molecule organic thin films. We succeeded to collect training data for this algorithm, and confirmed that it spans a wide range of thin film states (sub-monolayer to multilayer) using scanning tunneling microscopy. However, more training data is needed to run the optimization algorithm properly.
In addition, we created a new computational method which can, in principle, determine the atomic structure of an organic thin film from low-energy electron diffraction (LEED) data.
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