# High-throughput discovery of chemical structure-polarity relationships combining automation and machine learning techniques

arXiv: https://arxiv.org/abs/2202.05962 · HTML: https://mlsanity.com/paper/2202.05962/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 22 references that are in the map.
- First posted on arXiv 12 Feb 2022.

## Builds on (most cited first, 1 of 1)

- [Small Data Challenges in Big Data Era: A Survey of Recent Progress on Unsupervised and Semi-Supervised Methods](https://mlsanity.com/paper/1903.11260/) (2019)

## Built on by (most cited first, 4 of 4)

- [Retention Time Prediction for Chromatographic Enantioseparation by Quantile Geometry-enhanced Graph Neural Network](https://mlsanity.com/paper/2211.03602/) (2022)
- [Empowering Machines to Think Like Chemists: Unveiling Molecular Structure-Polarity Relationships with Hierarchical Symbolic Regression](https://mlsanity.com/paper/2401.13904/) (2024)
- [Transforming organic chemistry research paradigms: moving from manual efforts to the intersection of automation and artificial intelligence](https://mlsanity.com/paper/2312.00808/) (2023)
- [Intelligent Chemical Purification Technique Based on Machine Learning](https://mlsanity.com/paper/2404.09114/) (2024)

## Abstract (arXiv)

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.