Papers
3
Total Citations
248
H-Index
3
About
Tobias Osterrieder is at the forefront of accelerating materials discovery, pioneering the integration of high-throughput robotics and machine learning to revolutionize the development of next-generation photovoltaics. His research focuses on optimizing the complex parameter spaces of organic photovoltaics (OPV) and perovskite solar cells, two of the most promising thin-film technologies. Osterrieder’s major contribution lies in designing and deploying fully automated experimental platforms, such as the SPINBOT, which can intelligently and efficiently explore vast processing landscapes to identify optimal conditions for high-performance thin films. His landmark 2021 paper on using a robot-based platform and machine learning to unlock the full potential of OPV materials has garnered 168 citations, underscoring its significant impact on the field. This work, along with his subsequent 2023 study on machine learning-guided optimization of perovskite thin films (76 citations), demonstrates a powerful new paradigm for materials engineering. By replacing slow, manual trial-and-error with autonomous, data-driven experimentation, Osterrieder is dramatically accelerating the pace of innovation, paving the way for more efficient and commercially viable solar energy technologies.
Research Focus
Key Achievements
Top Papers
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