Metal halide perovskites are an emerging class of solution processable materials that have exhibited remarkable optoelectronic properties, such as high carrier mobility 1 , long diffusion length 2,3 , bandgap tunability 4,5 , high luminescence efficiency 6 and narrow emission bandwidth 7 . These properties, along with the ease of preparation of halide perovskite materials, have led to great advances in applications such as solar cells [8][9][10][11] , photodetectors 12,13 and light-emitting diodes (LEDs) [14][15][16][17] . The development of perovskite LEDs (PeLEDs) has, in particular, been rapid: in 2014 we reported electroluminescence (EL) from halide perovskites 14 and by 2018 we and others had achieved external quantum efficiencies of >20% 18-21 .

The visualization of data is indispensable in scientific research, from the early stages when human insight forms, to the final step of communicating results. In computational physics, 1 chemistry and materials science, it can be as simple as making a scatter plot, or as straightforward as looking through the snapshots of atomic positions manually. However, as a result of the "big data" revolution these conventional approaches are often inadequate. The widespread adoption of high-throughput computation for materials discovery and the associated community-wide repositories have given rise to data sets that contain an enormous number of compounds and atomic configurations. A typical data set contains thousands to millions of atomic structures, along with a diverse range of properties such as formation energies, band gaps, or bio-activities.It would thus be desirable to have a data-driven and automated framework for visualizing and analyzing such structural datasets. The key idea is to construct a low-dimensional representation of the data, which facilitates navigation, reveals underlying patterns, and helps to identify data points with unusual attributes. Such data-intensive maps, often employing machine learning methods, are appearing more and more frequently in the literature. However, to the wider community, it is not always transparent how these maps are made and how they should be interpreted. Furthermore, while these maps undoubtedly serve a decorative purpose in academic publications, it is not always apparent what extra information can be garnered from reading or making them. This Account attempts to answer such questions. We start with a concise summary of the theory of representing chemical environments, followed by the introduction of a simple yet practical conceptual approach for generating structure maps in a generic and automated manner. Such analysis and mapping is made nearly effortless by employing the newly developed software tool, ASAP. To showcase the applicability to a wide variety of systems in chemistry and materials science, we provide several illustrative examples, including crystalline and amorphous materials, interfaces, and organic molecules. In these examples, the maps not only help to sift through large datasets, but also reveal hidden patterns that could be easily missed using conventional analyses.The explosion in the amount of computed information in chemistry and materials science 2 has made visualization into a science in itself. Not only have we benefited from exploiting these visualization methods in previous works, we also believe that the automated mapping of datasets will in turn stimulate further creativity and exploration, as well as ultimately feed back into future advances in the respective fields.

Simple carbene complexes of copper halides give photoluminescence quantum yields of up to 96%, with sub-nanosecond emission lifetimes.