New QSPRs for Liquid Heat Capacity

Quantitative Structure-Property Relationships (QSPRs) have found applications in many areas of chemistry and engineering as effective prediction methods. QSPRs use molecular descriptors to simplify complex molecular properties to a single value and have been used extensively for constant value properties. Liquid heat capacity ( $$ ) is another property where QSPRs can be helpful prediction tools. Researchers have shown strong correlation between the $$ and various molecular descriptors, but these predictions are limited to a single temperature, usually 298.15 K. Additionally, other QSPRs have had problems with oxygen-containing functional groups. In this work, QSPRs for $$ at various temperatures were developed using data selected from the DIPPR database using a novel search method. This method improves on existing QSPRs for $$ by using unique descriptors but does not overcome the issue of oxygen-containing species.