Dataset Information

Prediction of the Auto-Ignition Temperatures of Binary Miscible Liquid Mixtures from Molecular Structures.

ABSTRACT: A quantitative structure-property relationship (QSPR) study is performed to predict the auto-ignition temperatures (AITs) of binary liquid mixtures based on their molecular structures. The Simplex Representation of Molecular Structure (SiRMS) methodology was employed to describe the structure characteristics of a series of 132 binary miscible liquid mixtures. The most rigorous "compounds out" strategy was employed to divide the dataset into the training set and test set. The genetic algorithm (GA) combined with multiple linear regression (MLR) was used to select the best subset of SiRMS descriptors, which significantly contributes to the AITs of binary liquid mixtures. The result is a multilinear model with six parameters. Various strategies were employed to validate the developed model, and the results showed that the model has satisfactory robustness and predictivity. Furthermore, the applicability domain (AD) of the model was defined. The developed model could be considered as a new way to reliably predict the AITs of existing or new binary miscible liquid mixtures, belonging to its AD.

SUBMITTER: Shen S

PROVIDER: S-EPMC6539801 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Prediction of the Auto-Ignition Temperatures of Binary Miscible Liquid Mixtures from Molecular Structures.

Shen Shijing S Pan Yong Y Ji Xianke X Ni Yuqing Y Jiang Juncheng J

International journal of molecular sciences 20190427 9

A quantitative structure-property relationship (QSPR) study is performed to predict the auto-ignition temperatures (AITs) of binary liquid mixtures based on their molecular structures. The Simplex Representation of Molecular Structure (SiRMS) methodology was employed to describe the structure characteristics of a series of 132 binary miscible liquid mixtures. The most rigorous "compounds out" strategy was employed to divide the dataset into the training set and test set. The genetic algorithm (G ...[more]

PMID: 31035591

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Dataset Information

Prediction of the Auto-Ignition Temperatures of Binary Miscible Liquid Mixtures from Molecular Structures.

Publications

Prediction of the Auto-Ignition Temperatures of Binary Miscible Liquid Mixtures from Molecular Structures.

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