High‐Accuracy Machine Learning Projections of Composition‐Dependent Thermal Stability in Halide Perovskites

Abstract

ABSTRACT Halide perovskites exhibit unpredictable properties in response to environmental stressors due to several composition‐dependent degradation mechanisms. In this work, we combine high‐throughput experiments, data visualization, and machine learning (ML) techniques to quantify correlations between composition, temperature, and material properties by analyzing high‐throughput, in situ environmental photoluminescence (PL) experiments. Correlation heatmaps show the influence of Cs content on film degradation, and dimensionality reduction visualization methods uncover clear composition‐based clusters despite overlapping datasets. A robust screening of 10 ML algorithms effectively forecasts PL features with single‐composition, composition‐generalized, and composition‐generalized stacking approaches, with the highest‐performing models achieving root mean squared errors of 1.84, 10.69, and 10.28, respectively. Using a multi‐output composition‐generalized Extra‐Trees and Ridge Regression stacked model, a full PL spectrum can be predicted for any time, temperature, and composition input. Our ML‐based framework could be expanded to other perovskite families, significantly reducing the analysis time to identify stable options for photovoltaics.