My work is concerned with developing novel modelling approaches -supported by advanced characterisation - bringing new physical insights of the process-microstructure-property relationships in advanced materials. I focus on fundamental problems to address the challenges of new materials and their future applications in aerospace, fuel cells, land vehicles, nuclear reactions and wind turbines. I work closely with industry and cover several material and technology applications that together are helping in the development of sustainable engineering solutions. Research in my group covers 4 main areas. 

Advanced Material Processing: The Achilles heel of any new material is its capacity to be made outside laboratory conditions. Advanced materials are designed with greater microstructural complexity demanding new predictive computational tools to optimise in-service performance and prevent process-related issues, e.g. cracks during rapid solidification or during non-isothermal forging. I endeavour in understanding the origins of defects and damage during advanced material processing using data science, physics-based modelling and correlative characterisation to design better material processing routes and ensure that new materials can be produced under industrial conditions. 

Towards a Hydrogen-based Economy: From H embrittlement to material design for H generation: Realising a “Hydrogen economy” is seen as a definite solution to tackle climate change and future energy demands. However, significant challenges remain in place. For instance, preventing Hydrogen related degradation is a grand challenge in Materials Science and anticipating its behaviour is critical to the viability of many materials used in safety-critical industries such as aerospace, energy, nuclear and automotive. We develop novel modelling strategies -based on combining atomistic, mesoscale and macroscale methods- that predict how H diffuses and interacts with microstructure to anticipate material’s response to H embrittlement. Similarly, we work on novel computational approaches based on atomistic and Machine Learning methods tackling the challenge of designing stable and resource-efficient material catalysts for H generation. Our approach is based on identifying key composition-structure-property links to provide new understanding on what are key material features controlling their electrochemical efficiency and improved stability. 

Computational Material Design: Advanced materials are designed to be more resilient but the trade-off and bottle-neck is that they are inherently more difficult to manufacture into components. They often require the use of tailored manufacturing routes involving as much effort as the material design process itself. I combine machine learning, physics-based modelling and material characterisation to design materials with improved mechanical and environmental performance whilst ensuring their appropriate manufacturability. For instance, we have been using mesoscale modelling, deep learning and nano-characterisation to design better and more resource-efficient steels and superalloys for aerospace and automotive applications.;

Engineering shear transformations in Advanced Structural and Functional Materials: Materials using shear transformations are widely used in high-strength/multifunctional structures, e.g. steels and shape memory alloys, and they are becoming more important in emerging manufacturing technologies, e.g. additive manufacturing. However, despite the importance of these transformations, our understanding remains mostly material-specific and very few predictive models are currently available, mostly due to the resulting microstructure being very complex. I work on deriving new physically-based models that predict such microstructural complexities in order to improve mechanical/functional properties of new materials using these transformations.