Structural and Electronic Features-Integrated Machine Learning Framework for High-Throughput Prediction of Organic Pollutant Reactivity.

Zonghao Liu; Shuang Wu; Ce-Hui Mo; Xiang Lei; Jianyu Han; Shaofeng Wang; Jie-Jie Chen; Han-Qing Yu; Min Song

doi:10.1021/acs.est.6c00875

April 29, 2026

Structural and Electronic Features-Integrated Machine Learning Framework for High-Throughput Prediction of Organic Pollutant Reactivity.

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Key Points

The aim is to develop a machine learning framework to predict the reactivity of organic pollutants.
Integrated structural and electronic features analysis
High-throughput predictive modeling
Focus on reactivity predictions for various organic pollutants.
The framework achieves a predictive performance of R2 = 0.811.
Facilitates the tailoring of oxidation processes to specific pollutant profiles.

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Implication

Reveals a predictive framework for organic pollutant reactivity, facilitating tailored oxidation processes.

Abstract

= 0.811). This work provides a transparent and high-throughput predictive framework for reactivity prediction of organic pollutants, facilitating the bottom-up design of an advanced oxidation process tailored to specific pollutant profiles.

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Liu et al. (2026) studied this question.

synapsesocial.com/papers/69f5945c71405d493afff307 https://doi.org/10.1021/acs.est.6c00875

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Structural and Electronic Features-Integrated Machine Learning Framework for High-Throughput Prediction of Organic Pollutant Reactivity.

Key Points

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Implication

Abstract

Discussion

Cite This Study

Structural and Electronic Features-Integrated Machine Learning Framework for High-Throughput Prediction of Organic Pollutant Reactivity.

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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