Machine learning boosts the discovery of new perovskite solar cell materials
Researchers in the CEST group have published a study demonstrating the effectiveness of machine learning methods to identify suitable perovskite solar cell materials. Perovskite solar cells are a novel technology gathering a lot of interest due to their high efficiency and potential for radically lower manufacturing costs when compared to the traditional silicon-based solar cells.
Despite their promising qualities, the commercialization of perovskite solar cells has been held back by their fast degradation under environmental stresses, such as heat and moisture. They also contain toxic substances that can negatively impact the environment. The search for new perovskite materials that do not have these problems is ongoing, but the established experimental and computational research methods have not been able to handle the high number of material candidates that need to be tried and tested.
CEST members Jarno Laakso and Patrick Rinke, with collaborators from University of Turku and China, developed new machine learning-based methodology for rapidly predicting perovskite properties. This new approach accelerates computations and can be used to study perovskite alloys. These alloy materials contain many candidates for improved solar cell materials, but studying them has been difficult with conventional computational methods. The researchers demonstrated the effectiveness of the new approach by finding the most stable mixing fractions for an alloy of CsPbCl3 and CsPbBr3 perovskites. Having an efficient method for studying the stability of perovskite alloys is a key step towards engineering solar cells that are more resilient to degradation.
The same methodology that was applied to perovskites in this study can boost the discovery of other new alloy materials. After the initial success with their machine learning approach, Laakso and collaborators are looking into studying more complex perovskite alloys to discover solar cell materials that are highly efficient, nontoxic, and resilient to degradation.
The paper was published in Physical Review Materials doi.org/10.1103/PhysRevMaterials.6.113801.
Read more news
Professor Hironori Yoshida: “Machines should adapt to materials, not the other way around”
Professor of Formgiving believes the future of design lies in embracing irregularity rather than eliminating it. His research combines design, AI and robotics.
President Ilkka Niemelä explains what the new vision for higher education and research means for Finland and Aalto
Aalto has the capability and the will to act as a trailblazer in implementing the vision.
Coming soon: Aalto ARTS Grad Show 2026
We're thrilled to invite you to the Aalto ARTS Grad Show 2026 of the School of Arts, Design and Architecture!