Monika Michalska
Papers
2
Total Citations
46
H-Index
2
About
Monika Michalska is a rising force in the field of next-generation photovoltaics, with a focused expertise in the high-throughput fabrication and optimization of quasi-2D Ruddlesden–Popper perovskite solar cells. Her major contribution lies in pioneering a methodology that synergizes machine learning with automated experimentation to rapidly navigate the immense compositional space of these materials. This approach, detailed in her highly cited 2023 work, directly addresses the critical stability challenges that have long hindered perovskite solar cells from commercial viability, moving beyond traditional trial-and-error methods. By demonstrating how artificial intelligence can accelerate the discovery of stable, high-performance formulations, her research has garnered significant attention, with her flagship paper accumulating over 40 citations in a short span. Michalska’s work not only provides a powerful blueprint for materials discovery but also establishes a new paradigm for integrating data science with experimental physics and chemistry. Her achievements mark her as a key innovator at the intersection of machine learning and renewable energy, offering a compelling path toward affordable, durable solar technology.
Research Focus
Key Achievements
Top Papers
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