# Intelligent Chemical Purification Technique Based on Machine Learning

arXiv: https://arxiv.org/abs/2404.09114 · HTML: https://mlsanity.com/paper/2404.09114/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 14 Apr 2024.

## Builds on (most cited first, 7 of 7)

- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Overfitting in adversarially robust deep learning](https://mlsanity.com/paper/2002.11569/) (2020)
- [Overfitting Mechanism and Avoidance in Deep Neural Networks](https://mlsanity.com/paper/1901.06566/) (2019)
- [Provable Generalization of Overparameterized Meta-learning Trained with SGD](https://mlsanity.com/paper/2206.09136/) (2022)
- [Retention Time Prediction for Chromatographic Enantioseparation by Quantile Geometry-enhanced Graph Neural Network](https://mlsanity.com/paper/2211.03602/) (2022)
- [High-throughput discovery of chemical structure-polarity relationships combining automation and machine learning techniques](https://mlsanity.com/paper/2202.05962/) (2022)

## Abstract (arXiv)

We present an innovative of artificial intelligence with column chromatography, aiming to resolve inefficiencies and standardize data collection in chemical separation and purification domain. By developing an automated platform for precise data acquisition and employing advanced machine learning algorithms, we constructed predictive models to forecast key separation parameters, thereby enhancing the efficiency and quality of chromatographic processes. The application of transfer learning allows the model to adapt across various column specifications, broadening its utility. A novel metric, separation probability (Sp), quantifies the likelihood of effective compound separation, validated through experimental verification. This study signifies a significant step forward int the application of AI in chemical research, offering a scalable solution to traditional chromatography challenges and providing a foundation for future technological advancements in chemical analysis and purification.