Abstract
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 ($S_p$), 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.
Abstract (translated)
我们报道了一种人工智能与柱色谱相结合的创新方法，旨在解决化学分离和纯化领域中的低效性和标准化数据收集问题。通过开发精确数据采集的自動化平台和应用先进的机器学习算法，我们构建了预测模型来预测关键分离参数，从而提高了色谱过程的效率和质量。传递学习的使用使模型能够适应各种柱形规格，从而扩大了其应用范围。一种新的指标，分离概率($S_p$)，通过实验验证了有效化合物分离的可能性。这项研究在应用人工智能技术进行化学研究方面取得了显著的进展，为解决传统色谱挑战提供了可扩展的解决方案，并为未来化学分析和纯化技术的进步奠定了基础。
URL
https://arxiv.org/abs/2404.09114
