Course summary
What if you could help transform how we discover life-saving medicines, advanced materials and sustainable chemicals by combining chemistry with the power of artificial intelligence (AI)? Our master’s in AI and Digital Chemistry is your gateway to this revolutionary frontier. Designed for future scientists ready to lead in an era of digital innovation, this programme empowers you with skills in AI, data science and computational chemistry – tools now essential to solving the world’s most complex chemical challenges. You’ll be based in the prestigious School of Chemistry, one of the UK’s largest and most research-active chemistry departments. Home to a vibrant community of over 160 postgraduate students and 60 postdoctoral researchers, the school offers an intellectually rich environment where groundbreaking ideas thrive. You'll also benefit from the university’s interdisciplinary strength, with collaborations across Mathematics, Computer Science, Engineering, Physical Sciences and industry leaders. A diverse range of MSc research projects are available, with opportunities to work with industry co-supervisors and access to the university’s high-performance computer, powered by some of the latest CPU and GPU technologies. Whether your interests lie in molecular discovery, process automation or AI-driven material design, you’ll apply AI and computational techniques to solve a chemical problem, gaining practical, industry-relevant experience. Graduating from this programme means stepping into a world of opportunity. Career paths include roles in pharmaceuticals, materials discovery, green chemistry and technology companies, where your unique blend of chemical insight and AI fluency will set you apart. This course is ideal for:
- Aspiring innovators: Individuals eager to apply AI techniques to solve complex chemical problems
- Future scientists: Those seeking a career in scientific research with a focus on data science and AI
- Problem solvers: Students who enjoy tackling challenging questions and developing new solutions
- Tech enthusiasts: Individuals interested in the latest advancements in AI, machine learning and high-performance computing
- Industry professionals: Those looking to enhance their skills and knowledge for roles in pharmaceuticals, materials discovery and tech companies
Modules
Core modules Machine Learning in Science – Part 1 This module will provide an introduction to the main concepts and methods of machine learning. It introduces the basics of supervised, unsupervised and reinforcement learning as applied to regression, classification, density estimation, data generation, clustering and optimal control. It will be taught via two sessions per week through a combination of fundamental concepts and hands-on applications. Machine Learning in Science – Part 2 This module will cover more advanced topics following from Machine Learning in Science Part 1, specifically the concepts and methods of modern deep learning. Topics include deep neural networks, CNNs, RNNs, GANs, LLMs, autoencoders, transfer learning, reinforcement learning, interpretable machine learning and Markov decision processes, cleaning data and handling large data sets The main project for the module is the self-driving PiCar
Assessment method
Modules are assessed with a mix of different methods, such as coursework, exams, written and oral reports and research projects. Assessment is varied and designed to reflect real-world skills. You will be awarded a Master of Science if you successfully achieve a weighted average mark of at least 50% with no more than 40 credits below 50%, and no more than 20 credits below 40%. You must also achieve a mark of at least 50% in the research project.
Entry requirements
2:1 BSc Hons in chemistry or a related subject (e.g. chemical engineering or natural sciences); or a combination of qualifications and/or experience equivalent to that level. A high 2:2 (above 56%) may be considered if you have relevant work experience or another supporting factor.
Fees and funding
Tuition fees
No fee information has been provided for this course
Tuition fee status depends on a number of criteria and varies according to where in the UK you will study. For further guidance on the criteria for home or overseas tuition fees, please refer to the UKCISA website .
Additional fee information
No additional fees or cost information has been supplied for this course, please contact the provider directly.
Provider information
University of Nottingham
University Park
Nottingham
NG7 2RD