Intelligent Chemical Purification Technique Based on Machine Learning
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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.
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References · 7
- 2018How Powerful are Graph Neural Networks?cited by 4,393
- 2019A Comprehensive Survey on Transfer Learningcited by 1,160
- 2020Overfitting in adversarially robust deep learningcited by 506
- 2019Overfitting Mechanism and Avoidance in Deep Neural Networkscited by 30
- 2022Provable Generalization of Overparameterized Meta-learning Trained with SGDcited by 9
- 2022Retention Time Prediction for Chromatographic Enantioseparation by Quantile Geometry-enhanced Graph Neural Networkcited by 8
- 2022High-throughput discovery of chemical structure-polarity relationships combining automation and machine learning techniquescited by 4
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