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-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 — Far-field electroluminescence mapping and Setfos cavity fitting for directional micro-LED emission
Rol, F., Altazin, S., Michit, N., Miralles, B., Ballot, C., Aventurier, B., De Martino, P., Le Maitre, P., & Simon, J. (2025). Study of far-field electroluminescence emission to enhance micro-LED performance. Journal of the Society for Information Display, 33, 1123-1134.
Research Paper: Determining the Width of the Dynamic Emission Zone in Light-Emitting Electrochemical Cells
Zhang, X., Ràfols-Ribé, J., Kirch, A., Larsen, C., & Edman, L. (2025), Determining the Width of the Dynamic Emission Zone in Light-Emitting Electrochemical Cells. Advanced Optical Materials.
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.