Prediction of normal melting point of pure substances by a reference series method

Abstract

The "Reference Series" method of Shacham et al. is modified to enable prediction of normal melting point temperatures (T m ) of pure substances within experimental error level. A homologous series for which large amount and high precision T m data are available is used as a "reference" series. To predict T m for a "target" series quantitative property-property relationships (QPPRs) are derived to represent the predicted T m values of the "target" series in terms of the T m values of the reference series. Two QPPRs are necessary in order to match the odd and even carbon number (n C ) oscillations of T m in the low n C region. In the high n C region, the QPPR is adjusted to represent correctly the asymptotic behavior of T m . It is shown that the method is very useful for consistency analysis of T m data and enables a reliable prediction of T m in both the low n C and the high n C regions.

A General Guidebook for the Theoretical Prediction of Physicochemical Properties of Chemicals for Regulatory Purposes

“…Other methods, such as the reference series method, have been proposed to predict T m for members of homologous series of pure substances from known values of molecules used as a "reference" in the same series. 240 From a general point of view, predictions for melting point based on the existing QSPR models are difficult. For the most recent ones, validated on external sets of compounds, predictions can be expected with an error around 30 K for organic compounds.…”

“…Other methods, such as the reference series method, have been proposed to predict T m for members of homologous series of pure substances from known values of molecules used as a “reference” in the same series …”

Development of quantitative structure property relationships for predicting the melting point of energetic materials

“…The training sets consisted of 24 Brauner and Shacham examined the melting point of structurally homologous series of compounds, which varied only in the number of carbon atoms present in the molecule. 23 Their multiple linear regression model was based only on functions of constitutional (number of carbon atoms) type descriptors and were regressed on melting points for varying series of long-chain hydrocarbon containing compounds (n-alkanes , n-alkanoic acids, n-alkenes, n-mercaptans, and n-alkyl acetates). The average absolute relative errors reported for the different sets were between 1% and 9%.…”