Advanced Materials Science (Data-driven Innovation) MSc

London, Stratford (UCL East)

The digital revolution and recent advents in data science, machine learning (ML) and artificial intelligence (AI) have sparked demand for next generation materials data scientists, able to utilise these emerging technologies for enhanced materials design and discovery. This programme will enable students to explore how the establishment of  Processing-Structure-Properties-Performance (PSPP) relationships can be significantly enhanced using data driven approaches.

UK students International students
Study mode
Full-time
UK tuition fees (2023/24)
£14,100
Overseas tuition fees (2023/24)
£35,000
Duration
1 calendar year
Programme starts
September 2023
Applications accepted
All applicants: 17 Oct 2022 – 31 Mar 2023

Applications open

Entry requirements

A minimum of a second-class Bachelor's degree from a UK university or an overseas qualification of an equivalent standard.

If your education has not been conducted in the English language, you will be expected to demonstrate evidence of an adequate level of English proficiency.

The English language level for this programme is: Level 1

UCL Pre-Master's and Pre-sessional English courses are for international students who are aiming to study for a postgraduate degree at UCL. The courses will develop your academic English and academic skills required to succeed at postgraduate level. International Preparation Courses

Further information can be found on our English language requirements page.

If you are intending to apply for a time-limited visa to complete your UCL studies (e.g., Student visa, Skilled worker visa, PBS dependant visa etc.) you may be required to obtain ATAS clearance. This will be confirmed to you if you obtain an offer of a place.

Equivalent qualifications

Country-specific information, including details of when UCL representatives are visiting your part of the world, can be obtained from the International Students website.

International applicants can find out the equivalent qualification for their country by selecting from the list below. Please note that the equivalency will correspond to the broad UK degree classification stated on this page (e.g. upper second-class). Where a specific overall percentage is required in the UK qualification, the international equivalency will be higher than that stated below. Please contact Graduate Admissions should you require further advice.

About this degree

This programme aims to equip students with state-of-the-art, advanced, comprehensive knowledge and expertise in data-driven materials science, including computational materials modelling and machine learning methodologies, to solve problems in materials science in the field of regression and classification, feature extraction, and data clustering.

The programme focuses on related  methodologies and technologies targeting the understanding of Process-Structures-Properties-Performance (PSPP) relations in advanced materials for energy, health and environmental applications of materials. Students will also learn to practice contemporary scientific research skills and gain desired insight and capability to be a researcher and/or an entrepreneur in the field. In addition, students will engage in a literature project and a six-month  research project related to the application of data-driven approaches to advanced materials and applications.

Who this course is for

This programme is designed for those with a background in physics, chemistry, polymers, materials science or biology. The course covers advanced physics-based materials modelling on all scales as well as data driven modelling.

What this course will give you

Advanced Materials Science (Data Driven Innovation) MSc provides you with the foundations of creating, managing and utilising materials data for extracting insight to enhance understanding and guide design and discovery of novel advanced materials for various contemporary and emerging applications. 

The programme aims to deliver innovative teaching fostering critical thinking, creativity and peer collaboration.

Students in this interdisciplinary programme benefit from UCL's emphasis on research-based learning and teaching and research input from departments across UCL in mathematical and physical sciences, and in engineering.
 

In addition to the specific skills and knowledge students acquire by taking this programme, they also develop managerial and entrepreneurship skills,  transferable skills in areas including literature survey, design of experiments, machine learning, materials research, critical data analysis and teamwork, and effective communication skills using real-life case scenarios and student-led group projects. Possible career pathways for graduates include computational modelling or data driven innovation including big data analytics, machine learning and digitalisation, targeting advanced materials for nanotechnologies, energy and environment, as well as advanced manufacturing and processing.

The foundation of your career

Graduates from this programme are prepared to enter a variety of fields such as aerospace, biomedical, engineering and other multidisciplinary industrials, with students being offered roles at companies including Shell, Johnson Matthey, Rolls-Royce, Merck, Oxford Instruments, Huawei, Bytedance, Procter & Gamble, Coca-Cola.

This programme has also provided students with an excellent foundation for the pursuit of further academic study such as another postgraduate degree or doctoral research. Graduates have gone on to be awarded full PhD studentships in Purdue University in the USA, Universities of Oxford, Queen Mary, Nottingham, Bath, St Andrews, and UCL in the UK, as well as other top universities in Australia and Hong Kong.

Employability

On graduation students will be equipped for a future career as a materials scientist or engineer in academia or industry, or as an entrepreneur.

Teaching and learning

The programme’s compulsory curriculum is assessed by a combination of different methods that typically include individual coursework, a group project and/or a written examination, individual research projects with associated presentations, dissertation reports and viva voce. Students are also given time to revise and work through their assessed work with opportunities for individual and group feedback.

Assessment methods vary according to modules and are designed to enable students to demonstrate learning over time.

Each taught module is normally lectured for 30 hours with around 20 hours tutorials and/or tutor-lead e-learning. The student is expected to spend around 100 hours on self-directed study for each taught module.

Modules

In Term 1 you will study compulsory modules relating to the microstructural control in advanced materials, material design, selection and discovery, and you will be exposed to the concepts of research design and research methods, thus gaining the necessary knowledge to develop your research project during the year. You will also start a route-specific compulsory module Integrated Data-driven Materials Science and Digitalisation.

In Term 2 you will further develop the skills gained in term 1, where you go on to undertake compulsory modules in Advanced Materials Characterisation, Advanced Materials Processing and Manufacturing as well as starting your six-month independent research project on cutting-edge topics on the field of multiscale materials modelling and data-drive materials science to be familiar with state-of-the-art methods and empowered with the specialized skills in materials modelling, machine learning, artificial intelligence and data science for materials discovery. You will further consolidate your knowledge and skills in data-driven materials innovation by studying the route-specific compulsory module Machine Learning and Data-driven Materials Science.

In Term 3 you will continue to engage in your research project, supported by your project supervisor. This will culminate in you presenting your research progresses and findings to your contemporaries in both written and oral presentation formats.

Please note that the list of modules given here is indicative. This information is published a long time in advance of enrolment and module content and availability is subject to change. Modules that are in use for the current academic year are linked for further information. Where no link is present, further information is not yet available.

Students undertake modules to the value of 180 credits. Upon successful completion of 180 credits, you will be awarded an MSc in Advanced Materials Science (Data-driven Innovation).

Accessibility

Details of the accessibility of UCL buildings can be obtained from AccessAble accessable.co.uk. Further information can also be obtained from the UCL Student Support & Wellbeing team.

Fees and funding

Fees for this course

UK students International students
Fee description Full-time
Tuition fees (2023/24) £14,100
Tuition fees (2023/24) £35,000

The tuition fees shown are for the year indicated above. Fees for subsequent years may increase or otherwise vary. Where the programme is offered on a flexible/modular basis, fees are charged pro-rata to the appropriate full-time Master's fee taken in an academic session. Further information on fee status, fee increases and the fee schedule can be viewed on the UCL Students website: ucl.ac.uk/students/fees.

Additional costs

Full-time overseas students are required to pay a fee deposit of £1,000 for this programme and part-time overseas students are required to pay a fee deposit of £500.

There are no programme-specific costs.

For more information on additional costs for prospective students please go to our estimated cost of essential expenditure at Accommodation and living costs.

Funding your studies

For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website.

Next steps

Students are advised to apply as early as possible due to competition for places. Those applying for scholarship funding (particularly overseas applicants) should take note of application deadlines.

There is an application processing fee for this programme of £90 for online applications and £115 for paper applications. Further information can be found at Application fees.

When we access your application we would like to learn:

  • why you want to study Advanced Materials Science at graduate level
  • why you want to study Advanced Materials Science at UCL
  • what particularly attracts you to the chosen programme
  • how your academic and professional background meets the demands of this challenging programme
  • where you would like to go professionally with your degree

Together with essential academic requirements, the personal statement is your opportunity to illustrate whether your reasons for applying to this programme match what the programme will deliver. Applicants who have a portfolio are strongly recommended to submit it when they apply.

Please note that you may submit applications for a maximum of two graduate programmes (or one application for the Law LLM) in any application cycle.

Choose your programme

Please read the Application Guidance before proceeding with your application.

Year of entry: 2023-2024

UCL is regulated by the Office for Students.