Achieving Better Photovoltaic Materials Faster with AI
16. Dezember 2024
Science publication: Researchers have shown how the use of machine learning greatly accelerates the search for new semiconducting molecules for perovskite solar cells.
Perovskite solar cells are considered a flexible and sustainable alternative to conventional silicon-based solar cells. Within a period of a few weeks, researchers have now found new organic molecules that can be used to increase the efficiency of perovskite solar cells. The international team includes scientists from the Helmholtz Institute Erlangen-Nürnberg for Renewable Energy (HI ERN), a branch office of Forschungszentrum Jülich. The scientists skilfully combined the use of AI with fully automated high-throughput synthesis. The strategy developed can be transferred to other areas of materials research, such as the search for new battery materials. The results of the research work were published in the renowned journal Science.
Original paper
Jianchang Wu, Luca Torresi, ManMan Hu, Patrick Reiser, Jiyun Zhang, Juan S. Rocha-Ortiz, Luyao Wang, Zhiqiang Xie, Kaicheng Zhang, Byung-wook Park, Anastasia Barabash, Yicheng Zhao, Junsheng Luo, Yunuo Wang, Larry Lüer, Lin-Long Deng, Jens A. Hauch, Dirk M. Guldi, M. Eugenia Pérez-Ojeda, Sang Il Seok, Pascal Friederich, Christoph J. Brabec: Inverse design of molecular hole-transporting semiconductors tailored for perovskite solar cells. Science, 2024. DOI 10.1126/science.ads0901.
Contact
- Institute of Energy Technologies (IET)
- Helmholtz Institute Erlangen-Nürnberg for Renewable Energy (IET-2 /HI ERN)
Room 367
Jessica Pölloth
Referentin Veranstaltungsmanagement und Öffentlichkeitsarbeit (HI ERN/IET-2)
- Institute of Energy Technologies (IET)
- Helmholtz Institute Erlangen-Nürnberg for Renewable Energy (IET-2 /HI ERN)
Room 5005
