To mitigate perovskites’ degradation, there have been a pressing need to identify the effects of environmental stressors on material physical behavior and device performance. We implement high-throughput environmental photoluminescence (PL) to interrogate the response of Cs-FA perovskites with a range of chemical composition while exposed to temperature and relative humidity cycles. These measurements are used as input when comparing how machine learning methods can be realized to forecast material response. We quantitatively compare linear regression, Echo State Network (ESN), and Auto-Regressive Integrated Moving Average with eXogenous regressors (ARIMAX).
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.