Ongoing Research
Explore the Materials 4.0 projects our students are currently researching
Find out moreCohort 1 projects
Explore the research being undertaken by our first cohort of students:
- Advancing battery intelligence using digital parameterisation of battery electrode microstructures – Sheffield
- Artificial Intelligence X-ray Imaging – Oxford
- Developing a machine learning based approach to 2D and 3D hydride characterisation in zirconium alloys – Manchester
- Developing the Next Generation of Polymers Using Artificially Intelligent Reactors – Sheffield
- Failure Fundamentals: understanding the role of hydrogen in jet engine failure – Imperial
- Machine learning for quantitative and qualitative defect analysis in semiconductors using hyperspectral cathodoluminescence – Cambridge
- Novel interphases for ceramic composites: a high-throughput approach – Imperial
Cohort 2 projects
Explore the research being undertaken by our second cohort of students:
- 5-dimensional data workflows to understand hydrogen embrittlement – Manchester
- A new generation of mechanistic models to understand the fatigue crack initiation behaviour of metals in hydrogen environments – Oxford
- Accelerated electrocatalyst design using artificial intelligence – Imperial
- Development of Fast Turnaround and Automated Approaches to the Characterisation of Bulk Aluminium Nitride Substrates – Cambridge
- Early stage failure prediction in fusion materials using machine learning – Sheffield
- High-throughput characterisation and inline process monitoring of 2D semiconductor films – Cambridge
- In-situ/operando hydrogen mapping with multi-modal X-ray imaging – Oxford
- Intelligent Materials Health Monitoring: Utilising Machine Learning to Ensure the Long-term Stability of Perovskite Solar Cells – Sheffield
- Microfluidic Fabrication and 3D Imaging of Metal and Metal-Oxide Aerogels – Leeds
- Multimodal Deep Learning for Mapping Hidden Phases in Ferroelastic Domain Wall Topologies for Dynamic Interactive Nano-Electronics – Imperial
- Particle properties by design – Leeds
- Towards in silico selection of interfacial actives: Discovery of new corrosion inhibitors for high value coating formulations – Manchester
- Understanding underlying chemical-physical mechanisms of polymer interactions with hot surfaces during polymer quenching of high value engineering components for management and control of microstructure and residual stress – Strathclyde
MATERIALS 4.0 THEMES
Cyber-physical systems, sensing, automation and robotics for materials innovation
Integrating physical materials processes with computational systems, utilising sensors, automation, and robotics to control and optimise experiments and manufacturing.
Data-centric approaches coupled with modelling and simulation
Using data as a central driver in materials research, combining it with advanced modelling and simulation techniques to predict material properties and performance, reducing the need for physical experiments.
Data curation, and standards for digital storage of materials-related data
Establishing best practices for organising, documenting, and storing materials data in a digital format, ensuring its accessibility, reliability, and interoperability for research.
Data-informed metrology for materials science
Using data analysis and machine learning to improve the accuracy, efficiency, and robustness of materials measurement techniques, enabling more precise characterisation of materials.
Development of materials-aware digital twins
Incorporating models of the response of materials to their environment into a digital twin so that it takes account of evolving material properties in use
Digitalisation in materials manufacturing
Integrating digital technologies such as automation, data analytics, and simulation into materials manufacturing processes to improve efficiency and reduce waste.
High-throughput making, characterisation and testing of materials
Developing automated systems and workflows to rapidly synthesise, characterise, and test large numbers of materials samples
Materials informatics, data-focused approaches and AI for materials discovery
Applying data science, machine learning, and artificial intelligence to extract knowledge from materials data, identify patterns, and predict new materials with desired properties
Novel coupling of experiment and simulation
Integrating experiment and computational simulations, allowing models to augment and steer experiments.
Novel data collection methods in materials applications
Developing innovative techniques for gathering materials data, such as using sensors, embedded systems, and advanced imaging.
“Smart” characterisation methods
Developing characterisation techniques that use real-time data processing, rapid feedback loops, and combinations of high- and low-fidelity methods.
Along with these areas of research in Materials 4.0, Royce has further activity in digital materials across our Research Areas, that is brought together under our “Modelling and Simulation” research area.