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Interpretable machine learning for green design of iron-based catalysts for efficient antibiotic degradation
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DOI：10.1016/j.gce.2026.04.001.png)
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Abstract
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• An interpretable ML framework is proposed to achieve rapid and low-cost antibiotic degradation via PMS activation by Fe-based oxide catalysts. • CatBoost is identified as the optimal predictor with the highest accuracy. • Interpretability analysis is conducted to elaborate the importance and interaction mechanism of features. • Interpretable ML is coupled with NSGA-III algorithm to resolve the critical tradeoff among antibiotic degradation process. • Optimized catalysts achieve high degradation rates and low dosage for the degradation of lomefloxacin hydrochloride and ofloxacin.
Keywords:
Iron-based oxide catalysts
Peroxymonosulfate activation
Interpretable machine learning
Multi-objective optimization
Antibiotic degradation
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