4.7 Article

Evaluating and elucidating the reactivity of OH radicals with atmospheric organic pollutants: Reaction kinetics and mechanisms by machine learning

Related references

Note: Only part of the references are listed.
Article Engineering, Environmental

Shedding light on Black Box machine learning models for predicting the reactivity of HO• radicals toward organic compounds

Shifa Zhong et al.

Summary: Developed a machine learning-assisted method for an environmental task, constructed QSAR models using deep neural networks and gradient boosting algorithms for predicting the reactivity of organic compounds towards HO radicals, and achieved satisfactory predictive performance with an ensemble model.

CHEMICAL ENGINEERING JOURNAL (2021)

Article Environmental Sciences

Aqueous picloram degradation by hydroxyl radicals: Unveiling mechanism, kinetics, and ecotoxicity through experimental and theoretical approaches

Flavio O. Sanches-Neto et al.

Summary: Pesticides play a key role in agricultural production, but their persistence in aquatic environments raises concerns about adverse effects on human health and the environment. This study used quantum chemistry and computational toxicology to analyze the mechanism, kinetics, and toxicity of picloram degradation, revealing potential harm of picloram and its degradation products, as well as their susceptibility to photolysis in sunlight. The results provide important insights for risk assessment and experimental research in aquatic environments.

CHEMOSPHERE (2021)

Article Engineering, Environmental

pySiRC: Machine Learning Combined with Molecular Fingerprints to Predict the Reaction Rate Constant of the Radical-Based Oxidation Processes of Aqueous Organic Contaminants

Flavio Olimpio Sanches-Neto et al.

Summary: A web application utilizing machine learning and molecular fingerprint algorithm was developed to calculate reaction rate constant of oxidative processes of organic pollutants in water. Three machine learning algorithms were evaluated, resulting in high goodness-of-fit for training set and good predictive capacity for test set. The model was able to interpret the reactivity of radicals based on the SHAP method.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2021)

Editorial Material Engineering, Environmental

The Global Legacy of POPs: Special Issue Comment

Steven J. Eisenreich et al.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2021)

Review Business

What Makes an Online Review More Helpful: An Interpretation Framework Using XGBoost and SHAP Values

Yuan Meng et al.

Summary: This paper investigates the prediction and explanation of online product reviews helpfulness, introduces feature sets covering review text and context cues, and uses gradient boosted trees models for analysis. Through real data analysis, it reveals the varying contributions of different features to the helpfulness of reviews in different product domains.

JOURNAL OF THEORETICAL AND APPLIED ELECTRONIC COMMERCE RESEARCH (2021)

Article Chemistry, Medicinal

Structural Analysis and Identification of False Positive Hits in Luciferase-Based Assays

Zi-Yi Yang et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2020)

Article Computer Science, Artificial Intelligence

From local explanations to global understanding with explainable AI for trees

Scott M. Lundberg et al.

NATURE MACHINE INTELLIGENCE (2020)

Article Environmental Sciences

Rate coefficients for the reactions of OH radical and ozone with a series of unsaturated esters

Yangang Ren et al.

ATMOSPHERIC ENVIRONMENT (2019)

Article Chemistry, Multidisciplinary

Temperature Dependence of Rate Processes Beyond Arrhenius and Eyring: Activation and Transitivity

Valter H. Carvalho-Silva et al.

FRONTIERS IN CHEMISTRY (2019)

Article Chemistry, Medicinal

Predictive Multitask Deep Neural Network Models for ADME-Tox Properties: Learning from Large Data Sets

Jan Wenzel et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

A machine learning approach to the accurate prediction of monitor units for a compact proton machine

Baozhou Sun et al.

MEDICAL PHYSICS (2018)

Article Environmental Sciences

Comparison of Machine Learning Techniques in Inferring Phytoplankton Size Classes

Shuibo Hu et al.

REMOTE SENSING (2018)

Article Chemistry, Medicinal

A Historical Excursus on the Statistical Validation Parameters for QSAR Models: A Clarification Concerning Metrics and Terminology

Paola Gramatica et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2016)

Article Chemistry, Physical

Stereodynamical Origin of Anti-Arrhenius Kinetics: Negative Activation Energy and Roaming for a Four-Atom Reaction

Nayara D. Coutinho et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2015)

Article Engineering, Environmental

Past, Present, and Future Controls on Levels of Persistent Organic Pollutants in the Global Environment

Luca Nizzetto et al.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2010)

Article Chemistry, Medicinal

y-Randomization and its variants in QSPR/QSAR

Christoph Ruecker et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2007)

Article Chemistry, Multidisciplinary

Validated QSAR prediction of OH tropospheric degradation of VOCs: Splitting into training-test sets and consensus modeling

P Gramatica et al.

JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES (2004)