Thomas Heumueller
Friedrich-Alexander-Universität Erlangen-Nürnberg, Forschungszentrum Jülich
Papers
7
Total Citations
468
H-Index
5
About
Thomas Heumueller is a materials scientist and photovoltaics researcher whose work sits at the dynamic intersection of solar energy technology, high-throughput automation, and machine learning. Based primarily within the organic and perovskite solar cell communities, Heumueller has made significant contributions to accelerating the discovery and optimization of next-generation photovoltaic materials through robotic experimentation and artificial intelligence-driven approaches. His most impactful work includes the development of autonomous, robot-based platforms capable of rapidly screening vast parameter spaces in both organic photovoltaics (OPV) and perovskite solar cells. His 2021 paper uncovering temperature-induced stability reversals in perovskite materials — garnering 174 citations — challenged prevailing assumptions about accelerated aging tests and reshaped how researchers evaluate cation engineering strategies. Equally influential is his high-throughput machine learning framework for OPV materials optimization (168 citations), which demonstrated the full potential of automated experimentation in unlocking material performance. Heumueller's pioneering contributions extend to self-driving laboratories for multicomponent OPV systems and the SPINBOT platform for thin-film engineering. Collectively, his research has fundamentally advanced the efficiency, stability, and rational design of solution-processed solar technologies, establishing him as a key innovator in next-generation renewable energy research.
Research Focus
Key Achievements
Top Papers
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