Quantifying and predicting long-term stability in realistic operating environments is of interest to a broad range of materials, from energy devices to marine and offshore environments - . However, accelerated environmental testing today is often time-intensive and expensive, limiting the speed of materials innovation. Commercial stability chambers can cost tens of thousands of US dollars, and may require additional modification to generate consistent-quality degradation data for a specific application . Given the rise of automated experimentation systems and artificial intelligence in materials discovery - , developing stability testing equipment that can both mitigate the cost of longitudinal testing and provide high quality, machine-interpretable degradation data grows in importance, addressing a bottleneck in high-throughput materials discovery.