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To optimize materials' stability, automated high-throughput workflows are ofincreasing interest. However, many of those workflows use processes notsuitable for large-area depositions which limits the transferability ofresults. While combinatorial approaches based on vapour-based depositions areinherently scalable, their potential for controlled stability assessments hasyet to be exploited. Based on MAPbI3 thin-films as a prototypical system, wedemonstrate a combinatorial inert-gas workflow to study materials degradationbased on intrinsic factors only, closely resembling conditions in encapsulatedde-vices. Through a comprehensive set of automated X-Ray fluorescence (XRF),X-Ray diffraction (XRD) and UV-Vis characterizations, we aim to obtain aholistic understanding of thin-film properties of pristine and aged thin-films.From phase changes derived from XRD characterizations before and after aging,we observe simi-lar aging behaviours for MAPbI3 thin-films with varying PbI2residuals. Using a custom-designed in-situ UV-Vis aging setup, thecombinatorial libraries are exposed to relevant aging conditions, such as heator light-bias exposure. Simultaneously, UV-Vis photospectroscopy is performedto gain kinetic insights into the aging process which can be linked tointrinsic degradation processes such as autocatalytic decomposition. Despitescattering effects, which complicate the conventional interpretation of in-situUV-Vis results, we demonstrate how a machine learning model trained on thecomprehensive characterization data before and after the aging process can linkoptical changes to phase changes during aging. Consequently, this approach doesnot only enable semi-quantitative comparisons of materials' stability but alsoprovides detailed insights into the underlying degradation processes which areotherwise mostly reported for investigations on single samples.

Advancing High-Throughput Combinatorial Aging Studies of Hybrid  Perovskite Thin-Films via Precise Automated Characterization Methods and  Machine Learning Assisted Analysis