Mar 25, 2019 09:29 AM EDT
The U.S. Department of Energy's (DOE) Argonne National Laboratory has developed a method on how to control the electronic structure of organic electronics.
The potential of organic electronics is great compared with the commonly used inorganic electronics in terms of versatility and high cost-efficiency. Companies can utilize their flexibility in printing these into paper or integrating them into clothing to provide power to wearable electronics.
A Maria Goeppert Mayer Fellow at the Argonne National Laboratory, Nick Jackson, showed that electrical efficiency is influenced by the internal structure of organic material. Organic materials have complex structures that challenge researchers to predict the final structure and efficiency of the material. Jackson focused on developing machine learning in order to make these predictions.
Organic materials are assembled through vapor deposition where an organic molecule evaporates in order to condense slowly on a surface. Conditions for deposition are manipulated in order to control the manner molecules are packed in the film.
"It's kind of like a game of Tetris," said Jackson. "The molecules can orient themselves in different ways, and our research aims to determine how that structure influences the electronic properties of the material."
The efficiency of the organic material is influenced by the charge mobility. Charge mobility is affected by the packing of the molecules in the film. The relationship between charge mobility and efficiency was studied by simulating vapor deposition process.
"We have models that simulate the behavior of all of the electrons around each molecule at nanoscopic length and time scales," said Jackson, "but these models are computationally intensive, and therefore take a very long time to run."
It is required that the behavior of electrons must be described in groups of molecules. This could be done if there are cheaper, coarser computational models developed that simulate the packing of entire devices. Computation time is reduced by these coarse models. The need to make these predictions jibe with the physical results is the challenge among scientists. Machine learning algorithms are used by Jackson to show the relationship between the detailed and coarse models.
"I drop my hands and leave it to machine learning to regress the relationship between the coarse description and the resulting electronic properties of my system," Jackson said.
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