  As an essential attribute of organic compounds, polarity has a profound
influence on many molecular properties such as solubility and phase transition
temperature. Thin layer chromatography (TLC) represents a commonly used
technique for polarity measurement. However, current TLC analysis presents
several problems, including the need for a large number of attempts to obtain
suitable conditions, as well as irreproducibility due to non-standardization.
Herein, we describe an automated experiment system for TLC analysis. This
system is designed to conduct TLC analysis automatically, facilitating
high-throughput experimentation by collecting large experimental data under
standardized conditions. Using these datasets, machine learning (ML) methods
are employed to construct surrogate models correlating organic compounds'
structures and their polarity using retardation factor (Rf). The trained ML
models are able to predict the Rf value curve of organic compounds with high
accuracy. Furthermore, the constitutive relationship between the compound and
its polarity can also be discovered through these modeling methods, and the
underlying mechanism is rationalized through adsorption theories. The trained
ML models not only reduce the need for empirical optimization currently
required for TLC analysis, but also provide general guidelines for the
selection of conditions, making TLC an easily accessible tool for the broad
scientific community.
