Hao Deng
Papers
2
Total Citations
46
H-Index
2
About
Hao Deng is a materials scientist and energy researcher specializing in next-generation photovoltaic technologies, with a particular focus on perovskite solar cells and the application of machine learning to accelerate materials discovery. His most notable work centers on quasi-two-dimensional Ruddlesden–Popper perovskite solar cells, a promising class of materials that address one of the field's most pressing challenges: the poor long-term stability that has historically hindered commercialization of organic–inorganic perovskite devices. Deng's most impactful contribution lies in pioneering a methodology that combines high-throughput fabrication with machine learning optimization to efficiently navigate the vast and complex compositional space of quasi-2D perovskites. This approach significantly reduces the time and resources required to identify high-performing, stable device configurations — a major bottleneck in solar cell research. Published in *Advanced Energy Materials* in 2023, this work has already garnered over 40 citations, reflecting its rapid uptake by the photovoltaics community. By bridging computational intelligence with experimental materials synthesis, Deng's research exemplifies a modern, data-driven paradigm in renewable energy science, offering a scalable pathway toward more efficient and commercially viable solar panel technologies.
Research Focus
Key Achievements
Top Papers
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