In our work in the current edition of the CMIH we have built up a strong pool of researchers and collaborations across the board from mathematics, statistics, to engineering, medical physics and clinicians. Our work has also confirmed that imaging data is a very important diagnostic biomarker, but also that non-imaging data in the form of health records, memory tests and genomics are precious predictive resources and that when combined in appropriate ways should be the source for AI-based healthcare of the future. Following this philosophy, the new CMIH brings together researchers from mathematics, statistics, computer science and medicine, with clinicians and relevant industrial stakeholder to develop rigorous and clinically practical algorithms for analysing healthcare data in an integrated fashion for personalised diagnosis and treatment, as well as target identification and validation on a population level. We will focus on three medical streams: Cancer, Cardiovascular disease and Dementia, which remain the top 3 causes of death and disability in the UK. Whilst applied mathematics and mathematical statistics are still commonly regarded as separate disciplines there is an increasing understanding that a combined approach, by removing historic disciplinary boundaries, is the only way forward. This is especially the case when addressing methodological challenges in data science using multi-modal data streams, such as the research we will undertake at the Hub. This holistic approach will support the Hub aims to bring AI for healthcare decision making to the clinical end users.

Models of complex chemical processes such as combustion or atmospheric chemistry assume that the molecules taking part are thermalized, that is that their energy is characterized by the temperature of the system. Chemical activation (CA) occurs when the energy released by a reaction is channelled into the products and they have an energy greater than would be thermally predicted. How does the reactivity of these activated species compare with their thermalized equivalents? What is the significance of CA? How can CA be incorporated into chemical models of complex systems? These are the questions at the heart of our project: Complex Chemistry and Chemical Activation (C3A). Aspects of CA have been known about for more than 100 years, indeed 2022 marks the centenary of the Lindemann Mechanism, the first theory proposed to explain the pressure dependence of some chemical reactions. Models of CA have grown in sophistication, yet uncertainties in key processes (energy transfer, calculation of densities of states) limit the accuracy of kinetic and thermodynamic predictions from such systems. Addressing the uncertainties in these aspects of current models through new experimental data and developments in fundamental models is one strand of C3A. More recently, work in this group and elsewhere has shown that systems which were thought to be adequately modelled by thermalized reagents, such as abstraction reactions (e.g. OH + HCHO), do need to considered in the context of chemical activation. In a 2018 review, Klippenstein states: 'These studies ultimately led us to the realization that at combustion temperatures, the foundational assumption of thermalization prior to reaction is not always valid, and further that its breakdown significantly affects key combustion properties' (Proceedings of the Combustion Institute, 36, p77). These phenomena are not limited to combustion; plasma chemistry and the atmospheric chemistry of Earth and other planets provide other important examples of applications. C3A is a collaboration between leading groups from Leeds and Oxford, both with interests in experiments and theory. C3A will generate a wealth of new experimental data, which in combination with theoretical interpretation, will allow us to assess the significance of CA in real systems and provide the tools to allow CA to be accurately incorporated into chemical models of of these processes. The impact of C3A to industry will be facilitated by collaborations with Shell, Dassault Systemes and AirLabs. Such models are essential tools for understanding important questions from current highly practical issues (how can combustion systems be optimized to minimize CO2 emissions and improve air quality) to future questions (biofuels for aviation, novel methods of renewable energy storage such as ammonia generation and combustion) to important, fundamental questions such as modelling the atmospheres of hot-Jupiter exo-planets or the interstellar medium. The accurate assessment and incorporation of CA into such models will significantly enhance their reliability and predictive value.

Many technological advances in modern-day life depend upon the development of new materials, or better control and understanding of existing materials. The chemical, mechanical and physical properties of materials depend on their constituent atoms and, in particular, their electrons. CASTEP is a state-of-the-art software package which uses quantum mechanics to predict the behaviour of those electrons and, hence, the material, and it is widely used by scientists in academia and industry. Many of these researchers are experimental scientists, rather than computational specialists, and the main aim of this proposal is to support them to use CASTEP more easily, efficiently and reliably, and to expand the user community by lowering the barrier of entry for new users. The work focuses on preparing CASTEP for the future, by improving its Usability, Sustainability, Efficiency and Reliability (USER) so any researcher can run it quickly, consistently and easily on any computer, from laptops to HPC facilities. The key challenges this proposal addresses are to: * enhance accessibility for non-specialist scientists * exploit future methods and technologies * take full advantage of available computing resources * further improve reliability, and be fully validated This far-reaching programme will improve the whole CASTEP user experience, including: re-imagining CASTEP's interface (focusing on scientific output, not algorithmic details) and creating comprehensive examples and tutorials; developing a deep API for embedding CASTEP in high-level workflows; automating CASTEP's parallel decomposition; and improving fault-tolerance. The work will be in collaboration with consortia (e.g. MCC, UKCP, CCP-NC, CCP9) and national experimental facilities (e.g. SuperSTEM), as well as industry partners (e.g. NVIDIA and BIOVIA). The ultimate, overarching goal is that CASTEP itself becomes 'invisible'; a hidden software infrastructure for providing quick, clear answers to research questions, whose correctness and successful operation may be taken for granted. The research described in this proposal will make significant impacts on many areas of academic and industrial research, particularly in materials for future technology. CASTEP is already used by well over 1000 academic groups and industrial research sites across the globe, including Johnson Matthey, Sony, Solvay, PG Corp, Pfizer, Astra Zeneca and Toyota, and supports research in a vast range of materials such as semiconductor nanostructures, ultra-high temperature ceramics, nanoscale devices, fluorophores, thermoelectrics, hybrid perovskites and solar cells, inorganic nanotubes and metal-air battery anodes. This work will promote CASTEP use across a diverse range of STEM disciplines, increase the effectiveness and impact of a wide variety of research initiatives, and enable researchers to directly address 5 of EPSRC's Grand Challenges in Physics, Engineering and Chemical Science.

Soft tissue related diseases (heart, cancer, eyes) are among the leading causes of death worldwide. Despite extensive biomedical research, a major challenge is a lack of mathematical models that predict soft tissue mechanics across subcellular to whole organ scales during disease progression. Given the tremendous scope, the unmet clinical needs, our limited manpower, and the existence of complementary expertise, we seek to forge NEW collaborations with two world-leading research centres: MIT and POLIMI, to embark on two challenging themes that will significantly stretch the initial SofTMech remit: A) Test-based microscale modelling and upscaling, and B) Beyond static hyperelastic material to include viscoelasticity, nonlinear poroelasticity, tissue damage and healing. Our research will lead to a better understanding of how our bodies work, and this knowledge will be applied to help medical researchers and clinicians in developing new therapies to minimise the damage caused by disease progression and implants, and to develop more effective treatments. The added value will be a major leap forward in the UK research. It will enable us to model soft tissue damage and healing in many clinical applications, to study the interaction between tissue and implants, and to ensure model reproducibility through in vitro validations. The two underlying themes will provide the key feedback between tissue and cells and the response of cells to dynamic local environments. For example, advanced continuum mechanics approaches will shed new light on the influence of cell adhesion, angiogenesis and stromal cell-tumour interactions in cancer growth and spread, and on wound healing implant insertion that can be tested with in vitro and in vivo systems. Our theoretical framework will provide insight for the design of new experiments. Our proposal is unique, timely and cost-effectively because advances in micro- and nanotechnology from MIT and POLIMI now enable measurements of sub-cellular, single cell, and cell-ECM dynamics, so that new theories of soft tissue mechanics at the nano- and micro-scales can be tested using in vitro prototypes purposely built for SofTMech. Bridging the gaps between models at different scales is beyond the ability of any single centre. SofTMech-MP will cluster the critical mass to develop novel multiscale models that can be experimentally tested by biological experts within the three world-leading Centres. SofTMech-MP will endeavour to unlock the chain of events leading from mechanical factors at subcellular nanoscales to cell and tissue level biological responses in healthy and pathological states by building a new mathematics capacity. Our novel multiscale modelling will lead to new mathematics including new numerical methods, that will be informed and validated by the design and implementation of experiments at the MIT and POLIMI centres. This will be of enormous benefit in attacking problems involving large deformation poroelasticity, nonlinear viscoelasticity, tissue dissection, stent-related tissue damage, and wound healing development. We will construct and analyse data-based models of cellular and sub-cellular mechanics and other responses to dynamic local anisotropic environments, test hypotheses in mechanistic models, and scale these up to tissue-level models (evolutionary equations) for growth and remodelling that will take into account the dynamic, inhomogeneous, and anisotropic movement of the tissue. Our models will be simulated in the various projects by making use of the scientific computing methodologies, including the new computer-intensive methods for learning the parameters of the differential equations directly from noisy measurements of the system, and new methods for assessing alternative structures of the differential equations, corresponding to alternative hypotheses about the underlying biological mechanisms.