AIMC Topic: Models, Chemical

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Advancing micro-nano supramolecular assembly mechanisms of natural organic matter by machine learning for unveiling environmental geochemical processes.

Environmental science. Processes & impacts Jan 22, 2025
The nano-self-assembly of natural organic matter (NOM) profoundly influences the occurrence and fate of NOM and pollutants in large-scale complex environments. Machine learning (ML) offers a promising and robust tool for interpreting and predicting t...
Machine Learning Organic Chemicals Environmental Pollutants Humic Substances Models, Chemical Environmental Monitoring
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Autonomous learning of generative models with chemical reaction network ensembles.

Journal of the Royal Society, Interface Jan 22, 2025
Can a micron-sized sack of interacting molecules autonomously learn an internal model of a complex and fluctuating environment? We draw insights from control theory, machine learning theory, chemical reaction network theory and statistical physics to...
Machine Learning Models, Chemical
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Artificial neural networks to estimate the sorption and desorption of the herbicide linuron in Brazilian soils.

Environmental pollution (Barking, Essex : 1987) Jan 15, 2025
Generally, herbicides used in Brazil follow manufacturer's recommendations, which often do not consider soil attributes. Statistical models that include the physicochemical properties of the soil involved in herbicide retention processes could enable...
Brazil Environmental Monitoring Soil Neural Networks, Computer Linuron Herbicides Soil Pollutants Models, Chemical Adsorption
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Neural network emulator for atmospheric chemical ODE.

Neural networks : the official journal of the International Neural Network Society Jan 2, 2025
Modelling atmospheric chemistry is complex and computationally intense. Given the recent success of Deep neural networks in digital signal processing, we propose a Neural Network Emulator for fast chemical concentration modelling. We consider atmosph...
Atmosphere Models, Chemical Neural Networks, Computer
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ReLMM: Reinforcement Learning Optimizes Feature Selection in Modeling Materials.

Journal of chemical information and modeling Dec 17, 2024
A challenge to materials discovery is the identification of the physical features that are most correlated to a given target material property without redundancy. Such variables necessarily comprise the optimal search domain in subsequent material de...
Reinforcement Machine Learning Models, Chemical
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Modeling the global ocean distribution of dissolved cadmium based on machine learning-SHAP algorithm.

The Science of the total environment Dec 12, 2024
Cadmium (Cd) is a bio-essential trace metal in the ocean that can be toxic at high concentrations, significantly impacting the marine environment and phytoplankton growth. Its distribution pattern is closely proportional to that of phosphate (PO), al...
Oceans and Seas Models, Chemical Water Pollutants, Chemical Seawater Cadmium Algorithms Environmental Monitoring Machine Learning
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Modeling and predicting caffeine contamination in surface waters using artificial intelligence and standard statistical methods.

Environmental monitoring and assessment Dec 5, 2024
Caffeine, considered an emerging contaminant, serves as an indicator of anthropic influence on water resources. This research employs various modeling techniques, including Artificial Neural Networks (ANN), Random Forest (RF), and more, along with hy...
Water Pollution, Chemical Water Pollutants, Chemical Environmental Monitoring Caffeine Models, Chemical Neural Networks, Computer Artificial Intelligence
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Simulation, prediction and optimization for synthesis and heavy metals adsorption of schwertmannite by machine learning.

Environmental research Nov 26, 2024
Due to its sea urchin-like structure, Schwertmannite is commonly applied for heavy metals (HMs) pollutant adsorption. The adsorption influence parameters of Schwertmannite are numerous, the traditional experimental enumeration is powerless. In recent...
Iron Compounds Metals, Heavy Adsorption Models, Chemical Water Pollutants, Chemical Machine Learning
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Molecular Design Based on Integer Programming and Splitting Data Sets by Hyperplanes.

IEEE/ACM transactions on computational biology and bioinformatics Oct 9, 2024
A novel framework for designing the molecular structure of chemical compounds with a desired chemical property has recently been proposed. The framework infers a desired chemical graph by solving a mixed integer linear program (MILP) that simulates t...
Machine Learning Models, Chemical Computational Biology Algorithms
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Emerging investigator series: predicted losses of sulfur and selenium in european soils using machine learning: a call for prudent model interrogation and selection.

Environmental science. Processes & impacts Sep 18, 2024
Reductions in sulfur (S) atmospheric deposition in recent decades have been attributed to S deficiencies in crops. Similarly, global soil selenium (Se) concentrations were predicted to drop, particularly in Europe, due to increases in leaching attrib...
Sulfur Models, Chemical Environmental Monitoring Soil Pollutants Europe Soil Selenium Machine Learning
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