Digital chemistry
Digital approaches to chemistry are becoming increasingly important, from robots performing chemical synthesis to machine learning supporting the interpretation of mass spectra. This theme brings together people working in these areas across the department.
Digital chemistry covers a wide range of activities across the department, either as research in its own right, or in supporting other research areas. The research theme aims to build a community equipped with the skills, capacity and capability to using new digital and artificial intelligence tools to enhance our capacity to make advanced discoveries within molecular science.
Research activities in this area include:
- Using machine learning approaches to predict, parameterize, optimise and explore a wide range of chemical reactions and systems (solvents development, atmospheric pollution, data analysis, spectral analysis, intelligent synthesis of dyes, liquid crystals etc).
- Developing new ways of automatically controlling experiments and analytical equipment through robotics and control systems.
- Developing and applying quantum chemical theory calculations to understand and predict molecular properties.
The research theme has a particular interest in the training of students and staff in digital chemistry techniques and is active in the University’s MSc in Data Science.
Staff
- Mat Evans (Theme Lead)
- Laurence Abbott
- Stephen Andrews
- Jon Agirre
- Alyssa-Jennifer Avestro
- Martin Bates
- Martin Cockett
- Kevin Cowtan
- Caroline Dessent
- Pete Edwards
- Ian Fairlamb
- Meghan Halse
- Jacqui Hamilton
- Peter Karadakov
- Alan Lewis
- Paul McGonigal
- Sarah Moller
- John Moore
- Killian Murphy
- Conor Rankine
- Andrew Rickard
- Paul Walton
- Derek Wann