AIMC Topic: Models, Chemical

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The use of artificial neural network for modelling adsorption of Congo red onto activated hazelnut shell.

Environmental monitoring and assessment
Activated hazelnut shell (HSAC), an organic waste, was utilized for the adsorptive removal of Congo red (CR) dye from aqueous solutions, and a modelling study was conducted using artificial neural networks (ANNs). The structure and characteristic fun...

Predicting Cd(II) adsorption capacity of biochar materials using typical machine learning models for effective remediation of aquatic environments.

The Science of the total environment
The screening and design of "green" biochar materials with high adsorption capacity play a pivotal role in promoting the sustainable treatment of Cd(II)-containing wastewater. In this study, six typical machine learning (ML) models, namely Linear Reg...

A parameter estimation method for chromatographic separation process based on physics-informed neural network.

Journal of chromatography. A
Chromatographic separation processes are most often modeled in the form of partial differential equations (PDEs) to describe the complex adsorption equilibria and kinetics. However, identifying parameters in such a model requires substantial computat...

Data-driven, explainable machine learning model for predicting volatile organic compounds' standard vaporization enthalpy.

Chemosphere
The accurate prediction of standard vaporization enthalpy (ΔH°) for volatile organic compounds (VOCs) is of paramount importance in environmental chemistry, industrial applications and regulatory compliance. To overcome traditional experimental metho...

Carbon-based molecular properties efficiently predicted by deep learning-based quantum chemical simulation with large language models.

Computers in biology and medicine
The prediction of thermodynamic properties of carbon-based molecules based on their geometrical conformation using fluctuation and density functional theories has achieved great success in the field of energy chemistry, while the excessive computatio...

Predictive modeling of copper (II) adsorption from aqueous solutions by sawdust: a comparative analysis of adaptive neuro-fuzzy interference system (ANFIS) and artificial neural network (ANN) approaches.

Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering
Heavy metal ions are considered to be the most prevalent and toxic water contaminants. The objective of thois work was to investigate the effectiveness of employing the adsorption technique in a laboratory-size reactor to remove copper (II) ions from...

Prediction of critical micelle concentration for per- and polyfluoroalkyl substances.

SAR and QSAR in environmental research
In this study, we focus on the development of Quantitative Structure-Property Relationship (QSPR) models to predict the critical micelle concentration (CMC) for per- and polyfluoroalkyl substances (PFASs). Experimental CMC values for both fluorinated...

Exploring Chemical Reaction Space with Machine Learning Models: Representation and Feature Perspective.

Journal of chemical information and modeling
Chemical reactions serve as foundational building blocks for organic chemistry and drug design. In the era of large AI models, data-driven approaches have emerged to innovate the design of novel reactions, optimize existing ones for higher yields, an...

The Impact of Interdisciplinary, Gender and Geographic Distributions on the Citation Patterns of the Journal of Chemical Information and Modeling.

Journal of chemical information and modeling
There has been a growing recognition of the need for diversity and inclusion in scientific fields. This trend is reflected in the Journal of Chemical Information and Modeling (JCIM), where there has been a gradual increase in the number of papers tha...

Artificial neural network-assisted thermogravimetric analysis of thermal degradation in combustion reactions: A study across diverse organic samples.

Environmental research
During gasification the kinetic and thermodynamic parameter depend on both the feedstock and the process conditions. As a result, one needs to enhance the understanding of how to model numerically these parameters using thermogravimetric analyzer. Co...