Publications
Here you will find research papers from our customers and our R&D team that use Fluxim’s R&D tools.
Research Paper — AI-Accelerated Synthesis of Perovskite Nanocrystals for Low-Cost Solar Cells
Zhao, X., Zhao, C., Jin, L., Zhang, X., Li, D., Zhu, W., Li, H., Li, Y., Wu, T., & Yuan, J. (2026). AI-accelerated scalable synthesis of nanocrystals for cost-effective photovoltaics. Nature Communications, 17(1), 11301. https://doi.org/10.1038/s41467-026-78268-4
Research Paper — AI-driven parameter extraction in perovskite solar cells
Jonas Diekmann, Zitong Yang, Nurlan Tokmoldin, Ziwei Liu, Francisco Peña-Camargo, Zhuofan Xiong, Paria Forozi Sowmeeh, Hongsheng Li, Qiuqiang Kong, Xiaoliang Ju, Felix Lang, Safa Shoaee, Dieter Neher, and Martin Stolterfoht
ACS Energy Letters 0, 0, pp
Research Paper — Machine learning unlocks impedance analysis for perovskite solar cells
Nabil, M., Grau, I., Grau-Crespo, R., Hamad, S., & Anta, J. A. (2026). Inversion of the impedance response towards physical parameter extraction using interpretable machine learning. Advanced Energy Materials.
Research Paper:Non-Invasive Composition Identification in Organic Solar Cells via Deep Learning
Chang, Y.-H.; Zhang, Y.-L.; Cheng, C.-H.; Wu, S.-H.; Li, C.-H.; Liao, S.-Y.; Tseng, Z.-C.; Lin, M.-Y.; Huang, C.-Y. (2025), Non-Invasive Composition Identification in Organic Solar Cells via Deep Learning. Nanomaterials, 15, 1112.
XGBoost trained on synthetic data to extract material parameters of organic semiconductors
E Knapp, M Battaglia, T Stadelmann, S Jenatsch, B Ruhstaller
8th Swiss Conference on Data Science, Lucerne, Switzerland, 9 June 2021
Deep ensemble inverse model for image-based estimation of solar cell parameters
M. Battaglia, E. Comi, T. Stadelmann, R. Hiestand, B. Ruhstaller, E. Knapp;
APL Mach. Learn. 1 September 2023; 1 (3): 036108.