Machine Learning for Material Performance Predictions Across Climates

Description

Ultimately, the true value of materials is defined by their real-world usage. Longevity and performance retention over time are often the main considerations. Rigorous laboratory testing is typically paired with extensive real-world evaluation to ensure a comprehensive assessment of material performance. However, this lengthy and demanding process slows down research and development efforts as well as the real-world deployment of new technologies. Emerging machine learning approaches provide opportunities to greatly accelerate this process.

Beckers Group is the number one supplier of coil coatings and a leading supplier of industrial paints worldwide. To deliver reliable recommendations and warranties to clients, Beckers has conducted decade-long studies on formulation weathering at testing sites worldwide, encompassing diverse climatic conditions. Previously [1], we demonstrated how this data can be combined with climatic information from satellite measurements to enable global predictions of coating performance at fine scales. In this project, we will leverage laboratory evaluation data and chemical similarity between formulations to deliver predictions earlier in the design process and drive material discovery. This project is part of the growing ecosystem at the intersection of material science and climate AI.

The project is multi-disciplinary, and we specifically welcome students with backgrounds in mathematics, physics, chemistry, engineering, and computer science. Previous experience in the areas of time-series analysis [2], spatio-temporal machine learning, graph neural networks, virtual sensing [3], as well as in AI for science is welcome. The student will have plenty of opportunities to growth and develop transferable skills in these areas.

This project will be supervised by Dr Vladimir Gusev (Dept. Computer Science) and an industry supervisor from the Beckers Group. Any informal enquiries about the project can be directed to Vladimir.Gusev@liverpool.ac.uk.

The global need for researchers with capabilities in materials chemistry, digital intelligence and automation is intensifying because of the growing challenge posed by Net Zero and the need for high-performance materials across multiple sectors. The disruptive nature of recent advances in artificial intelligence (AI), robotics, and emerging quantum computing offers timely and exciting opportunities for PhD graduates with these skills to make a transformative impact on both R&D and society more broadly.

The University of Liverpool EPSRC Centre for Doctoral Training in Digital and Automated Materials Chemistry is therefore offering multiple studentships for students from backgrounds spanning the physical and computer sciences to start in October 2025. These students will develop core expertise in robotic, digital, chemical and physical thinking, which they will apply in their domain-specific research in materials design, discovery and processing. By working with each other and benefiting from a tailored training programme they will become both leaders and fully participating team players, aware of the best practices in inclusive and diverse R&D environments.

This training is based on our decade-long development of shared language and student supervision between the physical, engineering and computer sciences, and takes place in the Materials Innovation Factory (MIF), the largest industry-academia colocation in UK physical science. The training content has been co-developed with 35 industrial partners and is designed to generate flexible, employable, enterprising researchers who can communicate across domains.

Applicant Eligibility

Candidates will have, or be due to obtain, a Master’s Degree or equivalent related to Physical Science, Engineering or Computational Science. Exceptional candidates with a First Class Bachelor’s Degree in an appropriate field will also be considered.

Application Process

Applicants are advised to apply as soon as possible no later than 17^th February 2025. The CDT will hold two rounds of applications assessment:

• Assessment Round 1: for all applications received between 11^th December 2024 – 15^th January 2025.
• Assessment Round 2: for all applications received between 16^th January 2025 – 17^th February 2025

Applicants who wish to be considered in Assessment Round 1 must apply by 15^th January 2025. Projects will be closed when suitable candidate has been identified (this could be before the 17th February 2025 deadline).

Please review our guide on “How to Apply” carefully and complete the online postgraduate research application form to apply for this PhD project in Computer Science.

We strongly encourage applicants to get in touch with the supervisory team to get a better idea of the project.

We want all our Staff and Students to feel that Liverpool is an inclusive and welcoming environment that actively celebrates and encourages diversity. We are committed to working with students to make all reasonable project adaptations including supporting those with caring responsibilities, disabilities or other personal circumstances. For example, if you have a disability you may be entitled to a Disabled Students Allowance on top of your studentship to help cover the costs of any additional support that a person studying for a doctorate might need as a result.