chenggoj/iGAM-MSI
iGAM-MSI is a repository containing code and trained machine learning models for studying Metal-Support Interactions (MSI) using Interpretable Generalized Additive Models (iGAM). This project leverages the power of iGAM to provide accurate and explainable predictions in materials science. The published work DOI associated with the codes is:
This tool helps materials scientists and chemists understand the complex relationships in Metal-Support Interactions (MSI). You can input your experimental or simulation data related to catalyst properties, and it provides clear, explainable predictions about MSI behavior. Researchers can use the insights to design more effective catalysts and supported metal systems.
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Use this if you need to understand not just what your materials science model predicts about Metal-Support Interactions, but also *why* it made those predictions.
Not ideal if you are looking for a black-box model that prioritizes prediction accuracy over explainability in your materials science research.
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Python
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MIT
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Last pushed
Sep 28, 2025
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