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Autoignition temperature: comprehensive data analysis and predictive models
Copyright policy )Autoignition temperature: comprehensive data analysis and predictive models
International audience; Here we report a new predictive model for autoignition temperature (AIT), an important physical parameter widely used to assess potential safety hazards of combustible materials. Available structure -AIT data extracted from different sources were critically analysed. Support vector regression (SVR) models on different data subsets were built in order to identify a reliable compound set on which a realistic model could be built. This led to a selection of the dataset containing 875 compounds annotated with AIT values. The thereupon-based SVR model performs reasonably well in cross-validation with the determination coefficient r 2 = 0.77 and mean absolute error MAE = 37.8°C. External validation on 20 industrial compounds missing in the training set confirmed its good predic-tive power (MAE = 28.7°C). ARTICLE HISTORY
- Université Paris Diderot France
- University of Strasbourg France
Microsoft Academic Graph classification: Computer science Mean absolute error computer.software_genre Set (abstract data type) Training set External validation Autoignition temperature Support vector machine Predictive power Generative topographic mapping Data mining computer
Data Analysis, Chemical Phenomena, fragment descriptors, Quantitative Structure-Property Prediction (QSPR), Quantitative Structure-Activity Relationship, Bioengineering, Fires, [CHIM.GENI]Chemical Sciences/Chemical engineering, autoignition temperature, Drug Discovery, support vector regression, Temperature, generative topographic mapping, General Medicine, Models, Chemical, Quantitative structure- property relationship (QSPR), Molecular Medicine, [CHIM.CHEM]Chemical Sciences/Cheminformatics
Data Analysis, Chemical Phenomena, fragment descriptors, Quantitative Structure-Property Prediction (QSPR), Quantitative Structure-Activity Relationship, Bioengineering, Fires, [CHIM.GENI]Chemical Sciences/Chemical engineering, autoignition temperature, Drug Discovery, support vector regression, Temperature, generative topographic mapping, General Medicine, Models, Chemical, Quantitative structure- property relationship (QSPR), Molecular Medicine, [CHIM.CHEM]Chemical Sciences/Cheminformatics
Microsoft Academic Graph classification: Computer science Mean absolute error computer.software_genre Set (abstract data type) Training set External validation Autoignition temperature Support vector machine Predictive power Generative topographic mapping Data mining computer
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).2 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).2 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!- Université Paris Diderot France
- University of Strasbourg France
International audience; Here we report a new predictive model for autoignition temperature (AIT), an important physical parameter widely used to assess potential safety hazards of combustible materials. Available structure -AIT data extracted from different sources were critically analysed. Support vector regression (SVR) models on different data subsets were built in order to identify a reliable compound set on which a realistic model could be built. This led to a selection of the dataset containing 875 compounds annotated with AIT values. The thereupon-based SVR model performs reasonably well in cross-validation with the determination coefficient r 2 = 0.77 and mean absolute error MAE = 37.8°C. External validation on 20 industrial compounds missing in the training set confirmed its good predic-tive power (MAE = 28.7°C). ARTICLE HISTORY