Applications of Artificial Intelligence to the Analysis of Chemical and Structural Data

The analysis of structural characterisation data is a time-consuming stage in the development of new functional materials. Recent rapid progress in the development of lab automation means that hundreds or even thousands of chemical experiments can be performed without significant human intervention. However, determining the outcome of these reactions can be challenging, particularly when the solid state structure is important in identifying promising new products. While the collection of solid characterisation data, such as X-ray diffraction, can be integrated into an automated workflow, analysing the data often still requires significant input from a human scientist, which hampers progress towards a truly autonomous lab workflow.
This PhD project aims to use artificial intelligence to deliver an automated process to analyse solid state characterisation data, initially focusing on powder X-ray diffraction. The use of machine learning methods to perform rapid analyses of data will be key to providing feedback on the outcome of experiments; information that can inform decisions about the next set of experiments, and hence a key component of achieving an autonomous chemistry lab.