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

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Empa-CT/PbI2_Residuals_From_UVVis

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Predicting PbI2 residuals from UV-Vis spectra

This repository contains all code and data used to train the ML model presented in the publication Advancing High-Throughput Combinatorial Aging Studies of Hybrid Perovskite Thin-Films via Precise Automated Characterization Methods and Machine Learning Assisted Analysis.

Please open "ML_UVVis.ipynb" to access the Jupyter Notebook in which the code is implemented. The file "environment.yml" includes all dependencies necessary to write the notebook within a conda environemnt.

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

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