AIMC Topic: Organic Chemicals

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Organic geochemical evidence for life in Archean rocks identified by pyrolysis-GC-MS and supervised machine learning.

Proceedings of the National Academy of Sciences of the United States of America Nov 17, 2025
Throughout Earth's history, organic molecules from both abiogenic and biogenic sources have been buried in sedimentary rocks. Most of these organic molecules have been significantly altered by geologic processes through deep time. Nonetheless, the na...
Organic Chemicals Plants Animals Phylogeny Pyrolysis Geologic Sediments Fossils Supervised Machine Learning Gas Chromatography-Mass Spectrometry
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High-Throughput Molecular Design of Donors and Non-Fullerene Acceptors for Organic Solar Cells Based on Convolutional Neural Networks.

Journal of chemical information and modeling Sep 16, 2025
Designing novel high-performance donor and acceptor molecules is essential for improving the power conversion efficiency (PCE) of organic solar cells (OSCs). However, conventional experimental methods for developing new materials are often time-consu...
Fullerenes Solar Energy Electric Power Supplies Convolutional Neural Networks Organic Chemicals Neural Networks, Computer
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Assessing biodegradability potential of organic chemicals in aquatic and soil environment through classification-based machine learning models developed in accordance with OECD standards.

The Science of the total environment Sep 2, 2025
Information on the biodegradation potential of organic chemicals in the ecosystem helps us analyze their persistence, bioaccumulation, and toxicity (PBT) behaviour. The environment is exposed to many chemicals from various sources, both intentionally...
Machine Learning Environmental Monitoring Water Pollutants, Chemical Soil Pollutants Soil Organic Chemicals Biodegradation, Environmental Organisation for Economic Co-Operation and Development
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Advancing Aqueous Solubility Prediction: A Machine Learning Approach for Organic Compounds Using a Curated Data Set.

Journal of chemical information and modeling Aug 10, 2025
Aqueous solubility is one key property of a chemical compound that determines its possible use in different applications, from drug development to materials sciences. In this work, we present a model for the prediction of aqueous solubility that leve...
Machine Learning Organic Chemicals Solubility Water
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Hierarchical machine learning-based prediction for ultrasonic degradation of organic pollutants using sonocatalysts.

Environmental research Aug 5, 2025
Ultrasound-based advanced oxidation processes (AOPs) are effective for degrading organic pollutants, with hydrogen peroxide (HO) acting as a key intermediate in radical generation and overall degradation efficiency. However, conventional machine lear...
Organic Chemicals Machine Learning Catalysis Water Pollutants, Chemical Hydrogen Peroxide Ultrasonic Waves Oxidation-Reduction
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A new approach methodology (NAM) for carcinogenicity prediction of organic chemicals using the multiclass ARKA framework and machine-learning-based stacking regression.

Journal of hazardous materials Jul 21, 2025
The accumulation of organic pollutants in the environment has significantly impacted the lives of flora and fauna, resulting in disruptions in the biological ecosystem. Carcinogenicity has been one of the most alarming adverse effects exhibited by th...
Environmental Pollutants Machine Learning Carcinogenicity Tests Carcinogens Organic Chemicals Quantitative Structure-Activity Relationship Algorithms Regression Analysis
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Machine learning prediction of DOC-water partitioning coefficients for organic pollutants from diverse DOM origins.

Environmental science. Processes & impacts Jul 16, 2025
This study aims to improve predictions and understanding of dissolved organic carbon-water partitioning coefficients (), a crucial parameter in environmental risk assessment. A dataset encompassing 709 datapoints across 190 unique organic pollutants ...
Humic Substances Carbon Models, Chemical Machine Learning Environmental Monitoring Organic Chemicals Water Pollutants, Chemical
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Robust S3Former deep learning model for the direct diagnosis and prediction of natural organic matter (NOM) from three-dimensional excitation-emission-matrix (3D-EEM) data.

Water research Jun 9, 2025
The non-destructive, three-dimensional excitation-emission matrix (3D-EEM) based on fluorescence spectroscopy has been widely used in natural organic matter (NOM) monitoring in aquatic environments. However, the direct recognition of the species and ...
Spectrometry, Fluorescence Humic Substances Environmental Monitoring Water Pollutants, Chemical Neural Networks, Computer Deep Learning Organic Chemicals
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First report on Quantitative Structure-Toxicity Relationship modeling approaches for the prediction of acute toxicity of various organic chemicals against rotifer species.

The Science of the total environment Apr 10, 2025
Nowadays, organic chemicals are crucial components in virtually every aspect of daily life, serving as indispensable elements for modern society. The ongoing synthesis of chemicals and the various potential harmful effects on living organisms are pro...
Water Pollutants, Chemical Linear Models Machine Learning Animals Organic Chemicals Rotifera Toxicity Tests, Acute Quantitative Structure-Activity Relationship
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Unsupervised Machine Learning-Based Image Recognition of Raw Infrared Spectra: Toward Chemist-like Chemical Structural Classification and Beyond Numerical Data.

Journal of chemical information and modeling Mar 19, 2025
Recent advances in artificial intelligence have significantly improved spectral data analysis. In this study, we used unsupervised machine learning to classify chemical compounds based on infrared (IR) spectral images, without relying on prior chemic...
Unsupervised Machine Learning Organic Chemicals Machine Learning Spectrophotometry, Infrared Cluster Analysis Principal Component Analysis Image Processing, Computer-Assisted
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