Insight into the contribution of individual functional groups to the flash point of organic compounds

Mathieu, Didier; Alaime, Thibaud

doi:10.1016/j.jhazmat.2013.12.047

Cited by 21 publications

(13 citation statements)

References 30 publications

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“…Finally, n a is the number of aromatic rings in the molecule, and V a is a volume increment that accounts for the role of aromaticity on the molar volume. Because the additive contributions to V m are designed on the basis of simple geometrical considerations, they have been referred to as geometrical fragments. , Accordingly, the present model is referenced as the GF method throughout this paper.…”

Section: Present Modeling Approachmentioning

confidence: 99%

Reliable and Versatile Model for the Density of Liquids Based on Additive Volume Increments

Mathieu

Bouteloup

2016

Ind. Eng. Chem. Res.

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Section: Present Modeling Approachmentioning

confidence: 99%

Reliable and Versatile Model for the Density of Liquids Based on Additive Volume Increments

Mathieu

Bouteloup

2016

Ind. Eng. Chem. Res.

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“…It is possible to get all parameters well-defined by dumping the C2-0 and N1-0 parameters into a single contribution associated with the cyano group. 48 However, this would make the present model unapplicable to azides. In fact, the values presently obtained for these two parameters are quite reasonable.…”

Section: Model Parametersmentioning

confidence: 93%

“…As a result, their number is the same for every molecule presently considered, except for triphenylmethyl azide where the terminal N atom belongs to a −N 3 group. It is possible to get all parameters well-defined by dumping the C2-0 and N1-0 parameters into a single contribution associated with the cyano group . However, this would make the present model unapplicable to azides.…”

Section: Model Parametersmentioning

confidence: 99%

Accurate or Fast Prediction of Solid-State Formation Enthalpies Using Standard Sublimation Enthalpies Derived From Geometrical Fragments

Mathieu

2018

Ind. Eng. Chem. Res.

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In view of its success in predicting sublimation enthalpies of molecular crystals near triple point conditions [Ind. Eng. Chem. Res. 2012, 51, 2814], the geometrical fragment (GF) approach is presently used to estimate standard values Δsub H 0 adjusted to the reference temperature of 298.15 K. The resulting GF0 scheme is fitted against theoretically confirmed Δsub H 0 data for 185 high nitrogen compounds and validated using organic compounds not used in the fitting process. It is subsequently used to predict the solid-state formation enthalpy Δf H 0(cr) of 40 energetic materials, combined with either correlated ab initio methods or simple models to estimate the gas-phase contribution. The combination of GF0 with ab initio data yields Δf H 0(cr) values with state-of-the-art accuracy. Combined with the simple atom pair contribution model, this procedure predicts Δf H 0(cr) with better accuracy and stronger physical grounds than alternative low-cost methods.

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“…We collected data from academic papers, the Gelest chemical catalogue, the DIPPR database, Lange's Handbook of Chemistry, the Hazardous Chemicals Handbook, and the PubChem chemical database . Basic properties of the flash point datasets from each paper (size, average, and standard deviation) are given in Table .…”

Section: Methodsmentioning

confidence: 99%

“…Statistics of extracted datasets from papers. Names refer to first author and year for the following references: Chen14 [19], Wang11 [14], Mathieu12 [23], Mathieu14 [27], Katrizky07 [28], Saldana11 [26], Le15 [21], Pan07 [22], Pan13 [24], Patel10 [25], Carroll10 [16], Carroll11 [15], Carroll15 [18], Goinho11 [20]. "Original size" is the size of the dataset taken from the reference.…”

Section: Introductionmentioning

confidence: 99%

Assessing Graph‐based Deep Learning Models for Predicting Flash Point

Sun

Krakauer

Politowicz

et al. 2020

Molecular Informatics

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Flash points of organic molecules play an important role in preventing flammability hazards and large databases of measured values exist, although millions of compounds remain unmeasured. To rapidly extend existing data to new compounds many researchers have used quantitative structure-property relationship (QSPR) analysis to effectively predict flash points. In recent years graph-based deep learning (GBDL) has emerged as a powerful alternative method to traditional QSPR. In this paper, GBDL models were implemented in predicting flash point for the first time. We assessed the performance of two GBDL models, messagepassing neural network (MPNN) and graph convolutional neural network (GCNN), by comparing against 12 previous QSPR studies using more traditional methods. Our result shows that MPNN both outperforms GCNN and yields slightly worse but comparable performance with previous QSPR studies. The average R ! and Mean Absolute Error (MAE) scores of MPNN are, respectively, 2.3% lower and 2.0K higher than previous comparable studies. To further explore GBDL models, we collected the largest flash point dataset to date, which contains 10575 unique molecules. The optimized MPNN gives a test data R ! of 0.803 and MAE of 17.8 K on the complete dataset. We also extracted 5 datasets from our integrated dataset based on molecular types (acids, organometallics, organogermaniums, organosilicons, and organotins) and explore the quality of the model in these classes.

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Insight into the contribution of individual functional groups to the flash point of organic compounds

Cited by 21 publications

References 30 publications

Reliable and Versatile Model for the Density of Liquids Based on Additive Volume Increments

Reliable and Versatile Model for the Density of Liquids Based on Additive Volume Increments

Accurate or Fast Prediction of Solid-State Formation Enthalpies Using Standard Sublimation Enthalpies Derived From Geometrical Fragments

Assessing Graph‐based Deep Learning Models for Predicting Flash Point

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