At present, lithium-ion batteries (LiBs) are most commonly used for electric vehicles and grid storage applications. However, LiBs have come under severe scrutiny for their environmental and social impacts caused by exploitative mining in the Global South. Moreover, they face severe challenges with regards to their supply chain including the ever-increasing demand of critical raw materials and the emergence of mining and manufacturing monopolies, which in turn has created significant price volatility. These supply chain weaknesses put the battery demand satisfaction, and with it the energy transition at risk. This fellowship proposal aims at advancing the development of aluminium-ion batteries (AiBs) as an innovative, sustainable, and resilient alternative to LiBs. To this end, I will employ a multidisciplinary research approach combining materials science with environmental, economic, policy, and supply chain considerations. Compared to LiBs, AiBs have the advantage of increased volumetric energy densities (increased amount of energy without increasing the size of the battery), lower supply chain risks (abundance of raw materials) and lower environmental footprint (the use of recycled aluminium can avoid the burden of ore processing). Despite these important advantages, AiBs are still under-researched and the battery performance falls short of its potential. Two primary challenges hinder their progress: 1) the cathode (electrical conductor) materials tested to date for AiBs demonstrate low performance and short lifetime, and 2) there is a significant knowledge gap regarding the underlying reactions that determine and hamper performance, impeding precise control of battery performance. With this fellowship, I lay out an ambitious programme to address these key technical challenges holding back AiB development. Here, I propose a novel materials design approach to explore a previously untapped pool of materials that could serve as potential AiB cathodes. The in-depth investigation of their fundamental electrochemical and molecular reaction mechanisms via sophisticated characterisation techniques during battery usage will create new knowledge that will be leveraged to identify performance bottlenecks, enabling the engineering of high-performance cathode materials for AiBs. This research proposal is strongly embedded in and guided by sustainability and resilience considerations of AiBs. My team and I will research synthesis methods informed by green chemistry principles to avoid lengthy and energy-intensive manufacturing processes. Moreover, we aim to use battery materials that are not only abundant and evenly distributed geographically, but also have minimal social and environmental impacts. We will apply life cycle assessment and techno-economic models evaluating the impacts across the AiB value chain to inform the battery materials design process. During the fellowship extension (+3 years), the development of AiBs will be continued towards up-scaling and prototyping, where the main challenges to be tackled will be the development of materials manufacturing processes suitable for up-scaling and the design of the battery cell. This research will benefit from a strong cross-disciplinary academic and industry network supporting the advancement of this exciting technology and the generation of global impact. This research not only pushes the limits of an emerging battery technology and sees through its advancement towards prototyping, but it will also support the alleviation of supply chain bottlenecks and geopolitical risks associated with current lithium-ion batteries. This will have significant academic impact via the creation of new knowledge while fostering societal and environmental benefits. Through the establishment of a robust green battery supply chain, this research will contribute to a resilient energy future.

The consortium aims to advance and lead in mesoscale science and engineering that are crucial to solving emerging societal challenges such as the net zero energy system, high-end manufacturing, healthcare and digital economy. This will be achieved by developing and exploiting cutting-edge mesoscopic modelling and simulation techniques with the aid of HEC (ARCHER2) and tier-2 GPU (Bede) systems. The UKCOMES community of academics, researchers, collaborators and end-users, already the largest and best in the world, will be consolidated and expanded to benefit the wider community and generate greater impact. Community codes as well as in-house codes will be further developed, disseminated and applied, using the best practices. The consortium, in working with CoSeC, will provide a stimulating, collaborative and interdisciplinary environment to train people in optimised use of current HEC and in preparation for the forthcoming exascale platforms, in order to conduct world-leading research and application. Mesoscales refer to those in between atomistic and macroscales. Such scales exist in almost all physical, chemical, biological, biomedical, material, pharmaceutical and engineering phenomena and processes. Mesoscales bridge atomistic and macroscales, and thus span many orders of magnitude. To resolve mesoscales is a great computational challenge, which requires ever more powerful HEC platforms. Unsurprisingly, mesoscale modelling and simulation has grown in capability and popularity in tandem with the development of HEC. In particular, the lattice Boltzmann method (LBM) has had a phenomenal growth in recent decades. Other methods under study that share the philosophy and aim of mesoscale modelling and simulation include dissipative particle dynamics (DPD), smoothed particle hydrodynamics (SPH), discrete velocity method (DVM), direct simulation Monte Carlo (DSMC), kinetic Monte Carlo (kMC), and coarse-grained MD (CGMD). Due to the pervasiveness and complexity of mesoscopic problems, the research and end-user communities working in the field are diverse and multidisciplinary. The consortium not only acts as a focal point for the diverse communities but also allows efficient utilisation of HEC resources through coordination and training. The remit of UKCOMES covers both simulation-methodology-orientated developments and application-driven research using HEC and tier-2 platforms. The work of the consortium will be pursued in the following work packages (WPs): (1) Community Codes Development, Optimisation & Dissemination; (2) Simulation & Optimisation of Net Zero Energy Systems; (3) Mesoscale Simulation & Design in Advanced Manufacturing; (4) Simulation & Application of Multiphase & Interfacial Flows; (5) Hemodynamics Simulation & Application in Healthcare; (6) VVUQ, Machine Learning & Data Analytics; (7) Engagement, Outreach, Dissemination and Impact Delivery. The Management Committee (MC) of UKCOMES consists of the PI and WP leaders. Strategic inputs are provided by the Scientific Advisory Board (SAB) and Industrial Advisory Board (IAB) as well as CoSeC and EPCC representatives. The MC sets the scientific agenda, reviews progresses, allocates computing resources, advises on software development and releases, organises training of people and plans for impact delivery. A set of transparent criteria will be applied to allocation of computing resources on ARCHER2 and Bede. Best practices will be formulated and disseminated within the consortium regarding how to port, benchmark, optimise and run codes. Throughout the project, the membership of UKCOMES will stay open to anyone who has interest in or can benefit from mesoscale modelling and simulation. The consortium will work with UK HEC consortia, tier-2, CCPs and ExCALIBUR communities to strengthen the UK base in CSE and build software as an important UK infrastructure. The consortium is also committed to international and industrial engagement.

The EPSRC Centre for Doctoral Training in Statistics and Machine Learning (StatML) will address the EPSRC research priority of the 'physical and mathematical sciences powerhouse' through an innovative cohort-based training program. StatML harnesses the combined strengths of Imperial and Oxford, two world-leading institutions in statistics and machine learning, in collaboration with a broad spectrum of industry partners, to nurture the next generation of leaders in this field. Our students will be at the forefront of advancing the core methodologies of data science and AI, crucial for unlocking the value inherent in data to benefit industry and society. They will be equipped with advanced research, technical, and practical skills, enabling them to make tangible real-world impacts. Our students will be ethical and responsible innovators, championing reproducible research and open science. Collaborating with students, charities and equality experts, StatML will also pioneer a comprehensive strategy to promote inclusivity, attract individuals from diverse backgrounds and eliminate biases. This will help diversify the UK's future statistics and machine learning workforce, essential for ensuring data science is used for public good. Data science and AI are now part of our everyday lives, transforming all sectors of the economy. To future-proof the UK's prosperity and security, it is essential to develop new methodology, specifically tailored to meet the big societal challenges of the future. The techniques underpinning such methods are founded in statistics and machine learning. Through close collaboration with a broad range of industry partners, our cohort-based training will support the UK in producing a critical mass of world-leading researchers with expertise in developing cutting-edge, impactful statistical and machine learning methodology and theory. It is well documented in government and learned society reports that the UK economy has an urgent need for these people. The significant level of industry support for our proposal also highlights the necessity of filling this gap in the UK data science ecosystem. StatML will learn from and build upon our previous successful experiences in cohort training of doctoral students (our existing StatML CDT funded in 2018, as well as other CDTs at Imperial and Oxford). Our students will continue to produce impactful, internationally leading research in statistics and machine learning (as evidenced by our students' impressive publication record and our world-leading research environment, as rated by the REF 2021 evaluation), while complementing this with a bespoke cohort-based Advanced Training program in Statistics and Machine Learning (StatML-AT). StatML-AT has been developed from our experience and in partnership with industry. It will be responsive to emerging technologies and equip our students with the practical skills required to transform how data is used. It will be delivered by our outstanding academics from both institutions alongside with industry leaders to ensure that students receive training in cutting edge technologies, along with the latest ideas in ethics, responsible innovation, sustainability and entrepreneurship. This will be complemented by industrial and academic placements to allow the students to develop their own international network and produce high-impact research. Together, StatML and its partners will train 90+ students over 5 cohorts. More than half of these will be funded from external sources, including 25+ by industry, representing excellent value for money. Our diverse cohorts will benefit from a unique and responsive training program combining academic excellence, industry engagement, and interdisciplinary culture. This will make StatML a vibrant research environment inspiring the next methodological advancements to transform the use of data and AI across industry and society.