CR3032 battery.Chemical data mining drives research into new organic semiconductors

　　Producing traditional solar cells made from silicon is very energy-intensive. Most importantly, they are stiff and fragile. Organic semiconductor materials, on the other hand, are flexible and lightweight. If only they were as efficient and stable as conventional cells, they would be a promising alternative. Karsten Reuter, Professor of Theoretical Chemistry at the Technical University of Munich, together with his team, are looking for new substances for photovoltaic applications, as well as displays and light-emitting diodes - OLEDs. Researchers have set their sights on organic compounds built on a framework of carbon atoms. Competitors for future electronics depend on their structure and composition. These molecules and the materials formed from them display a wide variety of physical properties, providing many promising candidates for future electronics. "Until now, a major problem has been tracking them: It takes weeks to months to synthesize, test and optimize new materials in the laboratory," Reuters said. "Using computational screening, we can dramatically speed up this process." Computers, not test tubes Researchers need neither test tubes nor Bunsen burners to find promising organic semiconductors. He and his team use powerful computers to analyze existing databases. This virtual search for relationships and patterns is called data mining. "Knowing what you are looking for is crucial in data mining," said project leader Dr. PD Harald Oberhofer. "In our case, it's electrical conductivity. For example, high electrical conductivity ensures that when sunlight excites molecules, large amounts of current flow in photovoltaic cells." Algorithm identifies key parameters Using his algorithm, he can search for very specific Physical parameters: For example, an important parameter is the "coupling parameter". The larger it is, the faster electrons move from one molecule to the next. Another parameter is the "reorganization energy": it defines the cost for a molecule to adapt its structure to a new charge after a charge transfer - the less energy required, the better the conductivity. The team used algorithms to analyze structural data for 64,000 organic compounds and group them into clusters. Results: Both the carbon-based molecular backbone and the "functional groups", i.e. compounds connected laterally to the central backbone, decisively influence the electrical conductivity. Using artificial intelligence to identify molecular clusters highlights structural frameworks and functional groups that promote favorable charge transport, making them particularly suitable for the development of electronic components. "We can now use it to predict the properties of molecules, but using artificial intelligence we can also design new compounds in which both the structural framework and the functional groups are very conductive," explains Reuter.

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