How the “Liquid Drop” Approach Could Be Efficiently Applied for Quantitative Structure–Property Relationship Modeling of Nanofluids

The main goal of this paper is the
evaluation of the applicability
of the geometrical “liquid drop” model (LDM) to describe
physicochemical properties of nanofluids in quantitative structure–property
relationship (QSPR) modeling. LDM-based descriptors are size-dependent,
which allows them to be applied for a series of nanoparticles with
the same chemical composition but different sizes. Thermal conductivity
of nanofluids as the target property was investigated. Random forest
regression as a nonparametric approach was utilized to determine important
structural features of nanofluids responsible for enhancing their
thermal conductivity.