ArticleACS omega2026
Comparative Evaluation of Deep Learning Graph Neural Networks and Classical Machine Learning Models for Predicting Heats of Combustion as a Key Hazard Indicator under GHS/CLP Standards.
Article in ACS omega, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
3 authors.
Funding
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Abstract
Accurate prediction of the heat of combustion (HoC) is essential for fuel design and chemical safety assessment. In this work, we systematically evaluate four machine learning modelsXGBoost, multilayer perceptron (MLP), graph convolutional network (GCN), and neural network convolution (NNConv)for their ability to predict HoC values across a data set of 4516 compounds. Our results show that descriptor-based approaches (XGBoost and MLP) demonstrated faster training times (≈34-60 s) and lower final test errors (2.08 and 2.31 kJ/g, respectively), with MLP achieving the highest coefficient of determination (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.