Dataset. Accelerate Flash Removal of PFAS from Soil by Human-guided Bayesian Optimization and Interpretable Machine Learning
Description
The study hypothesizes that coupling human-in-the-loop decision-making with Bayesian optimization and interpretable neural representations enables rapid identification of optimal FJH parameters and molecular-level insights into PFAS degradation mechanisms. Specifically, we posit that functional group composition, rather than chain length, primarily governs the defluorination efficiency under FJH conditions. The dataset contains 80 experimental records across four representative PFAS compounds. Each record combines: Molecular descriptors: molecular weight, number of fluorine atoms, mean C–F bond energy, polarity indices, and functional group identity. Experimental parameters: applied voltage, resistance, pulse duration, temperature rise rate, and calculated energy input.
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These data belong to the manuscript titled "Accelerate Flash Removal of PFAS from Soil by Human-guided Bayesian Optimization and Interpretable Machine Learning", which contains details regarding data processing and analysis.