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Abstract High-throughput materials synthesis methods, crucial for discovering novel functional materials, face a bottleneck in property characterization. These high-throughput synthesis tools produce 104 samples per hour using ink-based deposition while most characterization methods are either slow (conventional rates of 101 samples per hour) or rigid (e.g., designed for standard thin films), resulting in a bottleneck. To address this, we propose automated characterization (autocharacterization) tools that leverage adaptive computer vision for an 85x faster throughput compared to non-automated workflows. Our tools include a generalizable composition mapping tool and two scalable autocharacterization algorithms that: (1) autonomously compute the band gaps of 200 compositions in 6 minutes, and (2) autonomously compute the environmental stability of 200 compositions in 20 minutes, achieving 98.5% and 96.9% accuracy, respectively, when benchmarked against domain expert manual evaluation. These tools, demonstrated on the formamidinium (FA) and methylammonium (MA) mixed-cation perovskite system FA1−x MA x PbI3, 0 ≤ x ≤ 1, significantly accelerate the characterization process, synchronizing it closer to the rate of high-throughput synthesis.

In many emerging solar cell technologies, it is a significant challenge to extract the electronic properties of materials and interfaces inside a working device from experimental data. In many cases, approaches frequently used in mature technologies such as crystalline silicon are inapplicable as they require many material parameters to be known a-priori , which is rarely the case for novel materials. Based on this challenge for material and device characterization, this perspective discusses the different strategies for data interpretation that have been developed or are in the process of being developed for the specific case of halide perovskite solar cells. The specific focus of this work is to discriminate between experimental data and strategies to extract useful information from data. This information can then be used to make informed decisions about strategies for process and material innovations.