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Data analysis that uses the output of topological data analysis as input formachine learning algorithms has been the subject of extensive research. Thisapproach offers a means of capturing the global structure of data. Persistenthomology (PH), a common methodology within the field of TDA, has foundwide-ranging applications in machine learning. One of the key reasons for thesuccess of the PH-ML pipeline lies in the deterministic nature of featureextraction conducted through PH. The ability to achieve satisfactory levels ofaccuracy with relatively simple downstream machine learning models, whenprocessing these extracted features, underlines the pipeline's superiorinterpretability. However, it must be noted that this interpretation hasencountered issues. Specifically, it fails to accurately reflect the feasibleparameter region in the data generation process, and the physical or chemicalconstraints that restrict this process. Against this backdrop, we explore thepotential application of explainable AI methodologies to this PH-ML pipeline.We apply this approach to the specific problem of predicting gas adsorption inmetal-organic frameworks and demonstrate that it can yield suggestive results.The codes to reproduce our results are available athttps://github.com/naofumihama/xai_ph_ml

Notes on Applicability of Explainable AI Methods to Machine Learning Models Using Features Extracted by Persistent Homology