Adsorption properties of organoclay have been investigated
for
decades focusing on the morphology and physicochemical properties
of two-dimensional interlayers. Experimental studies have previously
revealed that the adsorption mechanisms depend on the molecular species
of the organocation and adsorbate, making it difficult to estimate
the adsorbed amount without experiments. Considering that the adsorption
of aromatic compounds has been reported by using various clays, organocations,
and adsorbates, machine learning is a promising method to overcome
the difficulty. In the present study, we collected adsorption data
from the literature and constructed models to estimate the adsorbed
amount of the organoclay by random forest regression. The composition
of the clay, molecular descriptors of the organocation and adsorbate
obtained by the RDKit, and experimental conditions were used as the
explanatory variables. Simple model construction by using all the
experimental data resulted in low R2 and
a mean absolute error. This problem was solved by the correction of
the adsorbed amount data by the Langmuir or Freundlich equation and
the following model construction at various equilibrium concentrations.
The plots of the adsorbed amount estimated by the latter model were
located close to the corresponding adsorption isotherm, while that
by the former was not. Thus, it was revealed that the adsorbed amount
was estimated quantitatively without understanding the adsorption
mechanisms individually. Feature importance analysis also revealed
that the combination of the organocation and adsorbate is important
at high equilibrium concentrations, while the clay should be selected
carefully as the concentration gets lower. Our results give an insight
into the rational design of the organoclay including the synthesis
and adsorption properties.