Advancing high-throughput combinatorial aging studies of hybrid perovskite thin films <i>via</i> precise automated characterization methods and machine learning assisted analysis
Alexander Wieczorek, Austin G. Kuba, Jan Sommerhäuser, Luis Nicklaus Caceres, Christian M. Wolff, Sebastian Siol
Abstract
UV-Vis results, we demonstrate how a machine learning model trained on the comprehensive characterization data before and after the aging process can link changes in the optical spectra to phase changes during aging. Consequently, this approach does not only enable semi-quantitative comparisons of material stability but also provides detailed insights into the underlying degradation processes which are otherwise mostly reported for investigations on single samples.
Topics & Concepts
Characterization (materials science)Materials scienceThin filmWorkflowComputer scienceDegradation (telecommunications)ThroughputStability (learning theory)ScalabilityPerovskite (structure)NanotechnologyProcess engineeringArtificial intelligenceBiological systemMachine learningChemical engineeringEngineeringDatabaseTelecommunicationsWirelessBiologyPerovskite Materials and ApplicationsQuantum Dots Synthesis And PropertiesMachine Learning in Materials Science