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Building Machine Learning Models for Reactivity Prediction in Radiation-Induced Graft Polymerization Using Interpretable Parameters

By Kiho Matsubara and others at
LogoGunma University
Machine learning prediction models for radiation-induced graft polymerization reactivity of methacrylate monomers were feasibly built with chemically interpretable parameters. The reactivity can be predicted based on the decision-tree-based machine learning algorithms. Among these algorithms, the XGBoost algorithm exhibited a good performance using five interpretable parameters: the solvation free energy of... Show more
March 30, 2023
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Building Machine Learning Models for Reactivity Prediction in Radiation-Induced Graft Polymerization Using Interpretable Parameters
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