AIMC Topic: Models, Chemical

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Modeling Soil pH at regional scale using environmental covariates and machine learning algorithm.

Environmental monitoring and assessment Jun 24, 2025
Soil pH serves as a critical indicator of soil chemistry and fertility, and mapping its spatial distribution holds significant importance for effective crop management. Digital soil mapping (DSM) is a commonly employed method for making rapid and cos...
Machine Learning Algorithms Support Vector Machine Models, Chemical Hydrogen-Ion Concentration Soil Environmental Monitoring
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Pyrolysis mechanism study on xylose by combining experiments, chemical reaction neural networks and density functional theory.

Bioresource technology Apr 11, 2025
Chemical reaction neural networks (CRNN) and density functional theory (DFT) are gaining attention in biomass pyrolysis mechanism research. Reaction pathways are often speculated based on a single method, influenced by expert knowledge. To address th...
Neural Networks, Computer Biomass Spectroscopy, Fourier Transform Infrared Pyrolysis Thermogravimetry Xylose Density Functional Theory Models, Chemical Kinetics Polysaccharides
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Explainable no-code OECD-compliant machine learning models to predict the mutagenic activity of polycyclic aromatic hydrocarbons and their radical cation metabolites.

The Science of the total environment Mar 18, 2025
Polycyclic aromatic hydrocarbons (PAHs) are persistent pollutants with well-known genotoxic and mutagenic effects, posing risks to ecosystems and human health. Their hydrophobic nature promotes accumulation in soils and aquatic environments, increasi...
Cations Organisation for Economic Co-Operation and Development Mutagens Risk Assessment Mutagenicity Tests Environmental Monitoring Environmental Pollutants Machine Learning Models, Chemical Polycyclic Aromatic Hydrocarbons
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Data-Driven Modeling and Design of Sustainable High Tg Polymers.

International journal of molecular sciences Mar 18, 2025
This paper develops a machine learning methodology for the rapid and robust prediction of the glass transition temperature (Tg) for polymers for the targeted application of sustainable high-temperature polymers. The machine learning framework combine...
Polymers Machine Learning Transition Temperature Glass Models, Chemical
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Current experimental, statistical, and mechanistic approaches to optimizing biomolecule separations in aqueous two-phase systems.

Journal of chromatography. A Mar 17, 2025
Aqueous two-phase systems (ATPS) have been used to purify a range of biomolecules, including small molecules, monoclonal antibodies, viruses, and whole cells. They are known for selective separations, creating a stabilizing, low-shear environment, an...
Thermodynamics Proteins Models, Chemical Water Models, Statistical
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Predicting surface soil pH spatial distribution based on three machine learning methods: a case study of Heilongjiang Province.

Environmental monitoring and assessment Mar 8, 2025
Comprehensive and accurate acquisition of surface soil pH spatial distribution information is essential for monitoring soil degradation and providing scientific guidance for agricultural practices. This study focused on Heilongjiang Province in China...
Hydrogen-Ion Concentration Soil China Support Vector Machine Machine Learning Environmental Monitoring Models, Chemical
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Physics-informed machine learning for automatic model reduction in chemical reaction networks.

Scientific reports Mar 7, 2025
Physics-informed machine learning bridges the gap between the high fidelity of mechanistic models and the adaptive insights of artificial intelligence. In chemical reaction network modeling, this synergy proves valuable, addressing the high computati...
Computer Simulation Models, Chemical Kinetics Alzheimer Disease Amyloid beta-Peptides Machine Learning Algorithms Humans
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Uncovering key sources of regional ozone simulation biases using machine learning and SHAP analysis.

Environmental pollution (Barking, Essex : 1987) Mar 6, 2025
Atmospheric chemical transport models (CTMs) are widely used in air quality management, but still have large biases in simulations. Accurately and efficiently identifying key sources of simulation biases is crucial for model improvement. However, tra...
China Environmental Monitoring Machine Learning Air Pollutants Ozone Models, Chemical Air Pollution
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Prediction of chlorination degradation rate of emerging contaminants based on machine learning models.

Environmental pollution (Barking, Essex : 1987) Mar 4, 2025
Assessing the degradation of emerging contaminants in water through chlorination is crucial for regulatory monitoring of these contaminants. In this study, we developed a machine learning model to predict the apparent second-order reaction rate const...
Environmental Monitoring Water Pollutants, Chemical Models, Chemical Machine Learning Water Purification Chlorine Halogenation
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Developing a machine learning-based predictive model for cesium sorption distribution coefficient on crushed granite.

Journal of environmental radioactivity Feb 4, 2025
The sorption of radionuclides on granite has been extensively studied over the past few decades due to its significance in the safety assessment of geological disposal for high-level radioactive waste (HLW). The sorption properties of granite for rad...
Cesium Silicon Dioxide Radioactive Waste Soil Pollutants, Radioactive Machine Learning Models, Chemical Radiation Monitoring Cesium Radioisotopes Adsorption
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