Premium
Illuminating Flash Point: Comprehensive Prediction Models
Author(s) -
Le Tu C.,
Ballard Mathew,
Casey Phillip,
Liu Ming S.,
Winkler David A.
Publication year - 2015
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201400098
Subject(s) - flash point , computer science , property (philosophy) , point (geometry) , flash (photography) , hazard , range (aeronautics) , explosive material , applicability domain , quantitative structure–activity relationship , machine learning , chemistry , mathematics , engineering , aerospace engineering , thermodynamics , physics , philosophy , geometry , organic chemistry , epistemology , optics
Flash point is an important property of chemical compounds that is widely used to evaluate flammability hazard. However, there is often a significant gap between the demand for experimental flash point data and their availability. Furthermore, the determination of flash point is difficult and costly, particularly for some toxic, explosive, or radioactive compounds. The development of a reliable and widely applicable method to predict flash point is therefore essential. In this paper, the construction of a quantitative structure – property relationship model with excellent performance and domain of applicability is reported. It uses the largest data set to date of 9399 chemically diverse compounds, with flash point spanning from less than −130 °C to over 900 °C. The model employs only computed parameters, eliminating the need for experimental data that some earlier computational models required. The model allows accurate prediction of flash point for a broad range of compounds that are unavailable or not yet synthesized. This single model with a very broad range of chemical and flash point applicability will allow accurate predictions of this important property to be made for a broad range of new materials.