Reviews & Analysis

Lead-based halide perovskite solar cells offer attractive power conversion efficiencies, but the release of lead into the environment is a major concern. Here, lead-free, tin-based perovskites are reviewed as an alternative, with a focus on how to extend their long-term stability.

Graph neural networks are machine learning models that directly access the structural representation of molecules and materials. This Review discusses state-of-the-art architectures and applications of graph neural networks in materials science and chemistry, indicating a possible road-map for their further development.

Inverted perovskite solar cells are promising for real-world energy harvesting, but suffer from issues with environmental stability. This Review discusses current understanding of stability in these devices and recent attempts to improve stability, as well as future directions that might enable their market roll-out.

Stable performance in solar cells is a key requirement for industrial success. Here, stability and degradation of perovskite solar cells are discussed within the context of the International Electrotechnical Commission’s standards for commercialized solar cells.

Hall effect measurements are often used to identify chiral spin textures in materials through the topological Hall effect, but similar Hall signals can arise due to sample inhomogeneity or experimental issues. Here, SrRuO3 is used as a model system to discuss the ambiguity in Hall signals, questioning the reliability of Hall effect measurements as evidence of chiral spin textures.