Self-optimizing process parameter screening in flow

一旦确定了特定步骤的合成策略，主要的挑战是找到一套最佳的浓度和加工条件。为此，重要的是以有效和可重复的方式筛选不同的条件，并收集分析数据。该项目将利用流动系统的超高吞吐能力。它将包括建立一个灵活的流动反应器，配备定制的在线和在线分析设备。它将受益于帝国理工学院吼叫设施的基础设施。捕获的数据将被用于机器学习算法，这些算法将在单独的项目中开发。目标是开发一种智能系统，该系统可以根据从一组反应参数中获得的知识，自动决定下一组条件，以预测和控制反应结果。

Once a step specific synthetic strategy has been qualified, a major challenge is to find an optimized set of concentrations and processing conditions. To this end, it is important to screen different conditions in an efficient and reproducible way, and to collect analytical data. This project will take advantage of the ultra-high throughput capability of flow systems. It will involve setting up a flexible flow reactor with bespoke in-line and at-line analytical equipment. It will benefit from the infrastructure that is available at Imperial's ROAR facility. The data captured will be subjected to machine learning algorithms that are to be developed in a separate project. The objective is to develop an intelligent system that that automatically decides on the next set of conditions based on its learnings from correlating a set of reaction parameters to predict and control the reaction outcome.