AIMC Topic: Models, Chemical

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Using Machine Learning to Predict First-Order Reaction Rate Constants of PFAS Degradation.

Bulletin of environmental contamination and toxicology
Per- and polyfluoroalkyl substances (PFAS) are environmentally persistent pollutants, posing challenges for effective remediation. This study presented a machine learning (ML) framework to predict the first-order reaction rate constant (k) of PFAS de...

A quantum-inspired attention integrated scalar long short-term memory model for accurate and stable groundwater contaminant source inversion.

Environmental monitoring and assessment
In groundwater contamination source inversion, concentration data from monitoring wells serve as the most crucial known information, directly affecting the inversion accuracy of unknown contamination source parameters. However, existing studies often...

Adsorption Energy Prediction Model for CO Reduction on Electrocatalysts Containing Previously Unencountered Metal Elements.

Journal of chemical information and modeling
Electrochemical carbon dioxide reduction (CORR) using electrocatalysts has gained attention for its potential to convert atmospheric CO into value-added chemicals. Recently, machine learning (ML) has emerged as a promising approach for catalyst devel...

Evolution of chromatographic modeling: From mechanistic models to hybrid models with physics-based deep learning.

Journal of chromatography. A
Hybrid modeling based on physics-based deep learning (PBDL) represents a transformative approach that unifies mechanistic understanding and data-driven learning, offering a pathway beyond the limitations of traditional chromatographic models. This re...

Predicting drug solubility in supercritical carbon dioxide green solvent using machine learning models based on thermodynamic properties.

Scientific reports
Reliable prediction of drug solubility in supercritical carbon dioxide (scCO₂) is crucial for the efficient design of pharmaceutical processes, including particle engineering and supercritical fluid-based extraction. Given that experimental determina...

Post ferric-substitution detection method optimization for Ni(II)-organic complexes measurement: Simulation, experimentation, and modeling.

Environmental monitoring and assessment
Nickel (Ni(II)) complexes, especially those formed with strong ligands such as ethylenediaminetetraacetic acid (EDTA), are difficult to quantify due to their low environmental concentrations and weak ultraviolet (UV) absorbance. These characteristics...

Development of prediction models on the degradation kinetics parameters of antibiotics in aquatic environments with machine learning methods.

Environmental science. Processes & impacts
Antibiotics, as emerging contaminants, are increasingly detected in aquatic environments, raising significant concerns about their ecological risks. However, the lack of hydrolysis rate constants () and aqueous hydroxyl radical degradation rate const...

Quantitative inversion of soil heavy metal pollution using a GA-BP neural network model.

Environmental monitoring and assessment
With the rapid development of industrialization in China, significant economic benefits have been accompanied by varying degrees of threat to the soil environment, particularly from heavy metal pollution. The rapid quantitative inversion of heavy met...

Sensitivity-driven control strategy and analysis of operating parameter MLSS in the stacking total nitrogen prediction model.

Environmental monitoring and assessment
The operation of wastewater treatment plants (WWTPs) is frequently characterized by complexity, largely attributable to the properties of the influent and the nonlinear fluctuations that occur throughout the wastewater treatment process. Accurate mod...

Simulation, prediction and optimization of heavy metal adsorption by metal-organic frameworks with machine learning.

Environmental research
The unique structures and complex characteristics of Metal-organic frame (MOFs) obscure understanding the processes and mechanisms of heavy metal (HM) removal. This study established an interpretable machine learning (ML) framework predicting adsorpt...