  High-throughput materials synthesis methods have risen in popularity due to
their potential to accelerate the design and discovery of novel functional
materials, such as solution-processed semiconductors. After synthesis, key
material properties must be measured and characterized to validate discovery
and provide feedback to optimization cycles. However, with the boom in
development of high-throughput synthesis tools that champion production rates
up to $10^4$ samples per hour with flexible form factors, most sample
characterization methods are either slow (conventional rates of $10^1$ samples
per hour, approximately 1000x slower) or rigid (e.g., designed for
standard-size microplates), resulting in a bottleneck that impedes the
materials-design process. To overcome this challenge, we propose a set of
automated material property characterization (autocharacterization) tools that
leverage the adaptive, parallelizable, and scalable nature of computer vision
to accelerate the throughput of characterization by 85x compared to the
non-automated workflow. We demonstrate a generalizable composition mapping tool
for high-throughput synthesized binary material systems as well as two scalable
autocharacterization algorithms that (1) autonomously compute the band gap of
200 unique compositions in 6 minutes and (2) autonomously compute the degree of
degradation in 200 unique compositions in 20 minutes, generating ultra-high
compositional resolution trends of band gap and stability. We demonstrate that
the developed band gap and degradation detection autocharacterization methods
achieve 98.5% accuracy and 96.9% accuracy, respectively, on the
FA$_{1-x}$MA$_{x}$PbI$_3$, $0\leq x \leq 1$ perovskite semiconductor system.
