Data Analysis of Cross-Coupling Reactions Using Enhanced Mathematical Methods to Decipher Complexity

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Vision: This PhD project is part of a strategically developed mini-Centre for Doctoral Training (CDT) in automating laboratory experiments, known as ALBERT, which is running as a pilot programme at the University of York between 2023-2024. The principal motivation for the ALBERT mini-CDT is to develop the science, engineering, and socio-technology that underpins the building of a laboratory-based robotic system for use in applied experiments across the physical sciences. Automated laboratory experiments are revolutionizing the way that we conduct our science, from a productivity, performance and efficiency perspective. Creating a Chemistry-based ecosystem that is cleaner, greener, safer, and cheaper than anything achievable by current conventional techniques and technologies, is a key driver for this research.Background: Research groups around the World are embedding robotic technologies and data analysis tools into their workflows to accelerate chemical reaction development and understanding, while improving productivity, cost-effectiveness, greenness and cleanliness. In our laboratory we have available to us two Chemspeed robotic systems for accelerating high throughput experimentation (HTE), which produces high volumes of chemical reaction data. This data has a richness to it, particularly relating to the outcomes of widely employed transition metal-catalysed cross-coupling reactions.In a study examining the reaction outcomes from a pharmaceutically relevant catalytic C-H bond functionalisation reaction we have demonstrated the value of analysing the reaction outcomes of a HTE campaign through the utilisation of mathematical methods such as principal component analysis, linear regression and clustering analysis tools. This work ultimately depends on having mathematical expertise in place to examine our reaction data in a rigorous, while ensuring appropriate controls are in place. High-profile research highlights the importance of having mathematical expertise as a cornerstone in the data analysis of chemical reactions, particularly using machine learning methods. The PhD project will thus be carried out by a graduate student with a mathematics undergraduate degree, with interdisciplinary interests.PhD project objectives:Manually assess HTE reaction data from topical synthetic cross-coupling reactions, draw trends and gain broader understanding.Automate data analysis of small (50-300) and large reaction (up to 5000) datasets.Development of a self-optimisation algorithm that can be implemented within the Chemspeed robotic working systems.Assess the impact of automated reaction optimisation routines, comparing against traditional means of working (pros and cons). The PhD student will work on data analysis, script writing and coding. The project is led by Prof. Ian Fairlamb. Several key co-supervisors are in place to play different roles in supporting this interdisciplinary project {Jessica Hargreaves (Maths), Darren Reed (Sociology) and Charlotte Willans (Chemistry)}. There will be collaborative aspects with other Fairlamb research group members and the wider ALBERT CDT programme, as it develops, particularly in its second year 2024/25. Key areas: Synthetic Chemistry, Digital Chemistry, Robotics, Mathematics