Team High Throughput Materials and Devices for PV receives funding as part of the Helmholtz AI Projects call
HI ERN is part of the AI-InSu-Pero project. As part of the project we will develop Artificial Intelligence Guided In-situ Analysis of Scalable Perovskite Thin Film Deposition
Helmholtz AI strengthens the application and development of applied artificial intelligence (AI) and machine learning (ML). In the second call for projects of Helmholtz AI, a top-class international panel of experts selected 17 collaborative high-risk, high-reward research projects. Helmholtz is investing a total of 6.6 million euros in these novel approaches – half of which is provided by the Association’s Initiative and Networking Fund.
“This year marks the bicentennial birthday of Hermann von Helmholtz, and this is a great opportunity to apply artificial intelligence and machine learning strategies to some of today’s grand challenges”, says Otmar D. Wiestler, the President of the Helmholtz Association. “The 17 selected projects use powerful AI and machine learning tools to address complex issues in their respective areas. I wish our scientists success and outstanding research results.”
62 project proposals were submitted in the second Helmholtz AI project call (up from 55 in the first round), demonstrating that this particular funding line is in persistent high demand. The 17 selected projects use novel analytical tools to solve pressing scientific and specific transfer challenges using AI. The projects promote the testing of these new approaches, are supported by several partners and will be implemented in up to three years – with the potential to quickly spawn larger follow-up projects.
The Project "AI-InSu-Pero - Artificial Intelligence Guided In-situ Analysis of Scalable Perovskite Thin Film Deposition" is one of the 17 selected projects. In AI-InSu-Pero we partner with KIT and Deutsches Krebsforschungszentrum to develop novel methods based on AI in order to improve Perovskite Solar Cells.
As part of the global endeavor to develop a sustainable energy portfolio, research on novel photovoltaic (PV) materials receives highest attention. The material class of solutionprocessed perovskite semiconductors stands out as exceptionally promising, but highly complex material systems due to entangled processes during the thin film formation. Perovskite-based solar cells are considered as a key building block of a future low cost and sustainable energy portfolio. Artificial intelligence (AI) methods have only significantly advanced over the past decade, but swiftly made their way into our daily life. As much as AI methods are already advancing some pioneering fields such as medical image diagnostics, they are suited to revolutionize combinatorial material research and material processing by accelerating computer vision tasks, pattern identification, feature extraction, and correlation analyses in experimental data. Along with these promises, this project will develop the AI algorithms required for detection of defects and inhomogeneities as well as film quality correlations in in-situ image data of solution-processed perovskite thin films. Moreover, in the spirit of a “high risk” project, we seek to realize an explainable AI model, i.e. make use of AI methods to enhance the understanding of process steps and dynamics during the perovskite thin-film formation. Achieving these objectives will define a major milestone and serve as a first step towards the development of AI for real-time deposition monitoring of highly complex materials.
Find out more about the Helmholtz AI.
CONTACT
Dr. Jens Hauch
Abteilungsleiter Teamleiter "High Throughput Materials and Devices"
Room 1.12