OptCliM will bring into climate modelling advances from mathematical optimization research. Our focus is upon parameterised processes that represent physics that are unresolved within climate models. These unresolved processes are represented through equations that include fixed parameters, with a typical climate model having around a hundred parameters. For example, thunderstorms not only generate heavy rain but are also one route for moisture into the atmosphere. One of the parameters expresses the rate at which moist air in the storm is mixed into the atmosphere. A range of values for each parameter is consistent with theory and measurement with changes in some parameters having a dramatic effect on future climate predictions. It is therefore necessary to have realistic parameter values in order to adequately model past or future climates. OptCliM responds to the need for an automatic and objective method to produce models consistent with reality. Currently the values used in climate models are chosen by manually adjusting several of them until the model produces an acceptable simulation of the current average climate. This process is very expensive in person time; it is not objective, not reproducible, and relies heavily on individual, if expert, judgement. OptCliM will develop iterative methods that use optimisation algorithms to automatically adjust many parameters so that models are consistent with observations. Beginning from any set of parameter values within the allowed ranges, the optimisation algorithm determines an initial set of model configurations to be run. On completion of these runs, the simulations are compared against the observations, and used to define parameter values for further runs until progress halts or the difference between simulation and observations are small. The challenges in applying such methods to climate models arise from the inherent noisiness of climate, and the computational expense of each model run. We will bring into climate modelling three alternative algorithms to find which is most effective in terms of making a model consistent with a range of different observations, and achieving that goal with minimum computing time and cost. OptCliM will: 1) Allow researchers to more easily generate parameter sets that produce realistic models allowing a better understanding of past and future climate change. 2) Provide an objective and transparent method to combine models and specified observations. 3) Through our impact plan contribute to the development of the new UK earth system model, UKESM1. 4) Open further development of methods for a more systematic exploration of uncertainty in climate modelling, for example generating parameter value sets that sample observational uncertainty to lead to a cloud of plausible models.

Geothermal energy provides an important alternative to fossil fuels, both for heating and for electricity generation. EGS (enhanced geothermal systems) enables the targeting of deep rock formations, at ~2 to 5 km depth for heat extraction. However, few attempts at EGS development have reached the commercial stage. A recent review identifies ~30 EGS sites in granites or other crystalline rocks worldwide, a large proportion of which have failed. One main reason is difficulty in developing EGS without generating unwanted seismicity. In the UK, the unsuccessful Rosemanowes project, in the Carnmenellis granite pluton in west Cornwall, was shut down in the early 1990s, after years of hydraulic fracturing failed to establish any significant inter-well hydraulic connection. This failure killed UK EGS R&D for a generation. Most recently, starting with drilling in 2019, a second project - at the United Downs site - has proceeded in the Carnmenellis granite. However, although the developer has not yet made any official announcement, for months the UK geothermal community 'grapevine' has discussed reasons why this project is in trouble, involving both seismicity and the lack of hydraulic connection between wells. This latest failure, involving the loss of a ~£20 million investment, highlights the need for greater expertise in EGS. Despite the body of research on reservoir stimulation, the general processes that govern the evolution of in-situ stress during reservoir stimulation, and the associated anthropogenic seismicity, still remain poorly understood. For example, how does chemical stimulation change the mechanical state of a fault surface? Will chemical reactions, creating new secondary minerals, alter the frictional properties of a fault in a manner that favours instability? How does the traction on a fault evolve as material is removed by dissolution? How do we manage fluid injection rates and pressures to avoid anthropogenic seismicity? This project aims to create a new multidisciplinary environment and identify key scientific questions that need to be addressed to mitigate risks of failure for future EGS projects. We have assembled a team of enthusiastic early-career and more senior researchers with high international standing and expertise in geoscience, geomechanics, and geophysics, from University of Glasgow (UG) in the UK, University of Wisconsin-Madison (UW) and Lawrence Berkeley National Laboratory (LBNL) in the USA, and Sinopec Research Institute of Petroleum Engineering (SRIPE) in China. Only by working together, can we use our complementary expertise, advanced laboratory facilities, unique field resources and site data to cover multiple scales and aspects that cannot be achieved by individual institutions. We will apply integrated laboratory, modelling and field approaches to develop new scientific understanding of how anthropogenic seismicity caused by geothermal reservoir stimulation can be controlled and eliminated. UW and LBNL will lead the experimental study using their laboratory facilities. The laboratory study will provide data for coupled modeling, which will be led by UG. SRIPE will lead field study and bring in unique resources and data from their Gonghe EGS site (the first and the most important EGS site in China). The field study at the unique Gonghe EGS site will provide vast future collaboration opportunities. We have also designed outreach and partnership activities to facilitate interaction and collaboration between researchers, and to develop long-term sustainable collaborations. These activities include two site visits (to Gonghe EGS site), annual 2-day workshops (in 2022 at UW and in 2023 at UG), 6 online smaller group meetings, and a project website. We expect this project will have significant impact on public and governmental attitudes to EGS in the UK and worldwide by contributing to evidence-based seismicity control and thus to breaking the existing pattern of EGS project failure.

In the world around us we are used to seeing tiled structures from patterns on clothes to floor coverings and artwork. Typically these structures are highly ordered and comprise symmetric arrays of their components. Our familiarity with such arrangements can lead us to overlook random tiling and quasi-crystalline structures that are present all around us. Indeed such structures are found in a broad spectrum of environments from glasses to Islamic art. We recently demonstrated that it is possible to create random tiled structures using molecules with carefully designed dimensions and intermolecular interactions. Such molecules can be considered as simple tiles and our original work has opened the possibility of studying the self-assembly of structures that are designed to avoid translational order. Our studies move beyond the conventional paradigms of supramolecular chemistry and are more akin to the behaviour of natural systems. The construction of random tiling systems, such as those that will be prepared, has great importance to a variety of disciplines, moving beyond chemistry and physics to scientists working on optimisation problems and statistical mechanics, to those researching quasi-crystals and the so-called spin-ice problem.

Buildings are the largest consumers of primary energy; consuming ~30% of it and also the building sector accounts for ~28% of total CO2 emissions globally. Recent studies have shown that by incorporating smart technologies such as the Internet of Things (IoT) into the buildings' energy system, energy savings of up to ~45 % are possible. IoT refers to a smart network of internet-connected everyday electrical and electronic devices which can communicate with each other and respond rapidly in real time. IoT-incorporated smart buildings have the promising potential to save our limited energy supply and reduce the waste of resources, money and time by continuously monitoring the different processes in buildings and optimising energy use. A smart building will utilise innumerable wireless sensors such as occupancy, humidity, temperature, proximity etc to monitor different processes and energy consumption. The latest market analysis (McKinsey & Company 2021) has shown that by 2030, the economic potential of IoT would range from $5.5 to 12.6 trillion and there would be more than 1 trillion connected devices. More than half of these devices and one-third of the economic value potential are expected to come from 'indoor' settings. How are we going to power these billions of connected devices? Connecting these sensor devices to the electrical grid is unfeasible as it requires extensive and complex installation and wiring, restructuring of the buildings, and limits the sensors' portable deployability across the buildings. The use of batteries is not sustainable as the limited lifespan of the batteries brings service interruptions during a battery replacement, increases maintenance costs, and poses severe environmental issues at their disposal. Moreover, once IoT has reached its projected wireless sensor nodes of one trillion, millions of battery replacements would be required per day which is unsustainable and impractical. My proposed research will bring a practical solution to this by developing inexpensive and environmentally friendly power sources by harvesting the freely available energy inside the buildings such as light from artificial light sources, heat energy and mechanical energy from electrical appliances which are otherwise lost as a wasted form of energy. For this, I will tune the properties of a family of electronic materials called 'hybrid perovskites'. The two physical properties that I envisage exploiting for this 'multiple' energy harvesting are (a) photovoltaic - converting light to electricity and (b) piezoelectricity - converting mechanical vibrations to electricity. The hybrid energy harvesters that I develop will make the IoT technology more sustainable by reducing their sole dependence on batteries, and accelerate the wide acceptance of IoT in other applications such as in complete digitisation of manufacturing (industry 4.0), health care, agriculture, precision farming, smart city and transportation settings. In addition to the IoT, the hybrid harvesters that I develop will make other emerging technologies such as Wearables more sustainable and the associated data collection, especially related to health monitoring, more reliable.

This programme uses recent advances made by the Oxford group in producing and controlling trains of amplified, ultrashort laser pulses, as well as a new method for producing high-energy photons in waveguides, and seeks to apply this to generating very bright sources of coherent, ultrashort, soft x-ray radiation - laser-like pulses of radiation at energies approaching x-ray photon energies, with extremely short time durations.Coherent soft x-ray radiation can be produced through a process called high harmonic generation . In this process multiple frequencies of an intense laser pulse can be generated by ionizing an atom and colliding the resulting electron into its parent ion, producing a pulse of radiation which can be as short as 100 attoseconds (1 attosecond = 1 billionth of a microsecond). Photon energies approaching x-ray energies can be generated this way. However, the efficiency of this process is very low, and gets much lower at higher energies, severely limiting many applications. The main cause for this low efficiency is the effect of dephasing . Simply put, this means that the laser pulse and the generated soft x-rays travel at different speeds, preventing the continuous growth of the soft x-ray. One way to overcome this dephasing is to employ a technique known as quasi phase-matching, whereby the harmonic generation process is switched on and off with a period equal to the coherence length , i.e. the distance over which the driving laser pulse and the generated radiation become out of phase. If this is achieved over N coherence lengths the soft x-ray signal will increase by a factor N-squared. Therefore, to maximize the power of this technique it is necessary to quasi phase-match over as many coherence lengths as possible. Switching the harmonic process on and off can be achieved in a number of ways. One method involves using a series ( or train ) of closely spaced, very short laser pulses travelling in the opposite direction to the generating laser pulse. Under EPSRC grant EP/C005449 the Oxford group has recently developed a method for producing trains of laser pulses with up to 100 pulses which can be accurately controlled. This has the potential to match up to 100 coherence lengths, which would increase the soft x-ray yield by a factor of 10,000! The adaptive nature of these pulse trains will also allow genetic algorithms to be employed to fully optimize the soft x-ray source. As part of this programme the group will seek to exploit the power of these new adaptive pulse trains to produce a soft x-ray source from harmonic generation with a brightness far exceeding anything previously recorded. Under grant EP/C005449 the Oxford group, in collaboration with a group at Queen's University Belfast (QUB), also discovered a new method for controlling the harmonic process. This relies on manipulating the way a laser pulse propagates through a narrow, hollow glass fibre. Through precise control of the laser pulse and how it couples into the fibre it is possible to excite modes in the fibre such that they create an interference pattern within the fibre which switches the harmonic generation on and off. The Oxford and QUB groups successfully used such a pattern to create the brightest source of water window x-rays from harmonic generation ever reported and, as part of this program will seek to develop this method even further. The development of such bright soft x-ray sources is crucial for a variety of scientific applications such as ultrafast measurements of chemical reactions, high-contrast biological imaging, advanced lithography, atomic and molecular spectroscopy, and ultrafast x-ray diffraction.