As we approach the theoretical limit of 3 nm transistor channel lengths, manufacturing challenges of CMOS architectures become exponentially more difficult and more expensive to overcome. Simultaneously, a seismic shift is occurring in the computational workload, away from offline processing to real-time big-data applications driven by the Internet of Things (IoT), robotics and autonomous agents. This combination of factors has led to an intensified exploration of alternative computing methodologies that span the entire Boolean computational stack from physical effects, to materials, devices, architectures, and data representations. It also includes novel, non-Boolean methods of computing such as quantum, wave and neuromorphic computation, Boltzmann machines and others. Exactly which combination of computational elements will evolve from this plethora of options is far from clear. However, it is possible to state general requirements future computing platforms must meet. First, any new computing methodology must be compatible with the existing multi-trillion-pound infrastructure associated with current CMOS based computing. Second, it must be scalable through multiple generations of incremental hardware and software improvements. Third, the performance/cost metric must greatly exceed that of Boolean CMOS processors, and, fourth, the new technology must provide a much more energy-efficient alternative to existing technology. Reservoir Computing (RC) leverages fast nonlinear dynamics in analogue physical systems to map a system's spontaneous transient response to solutions of traditionally hard problems such as classification tasks and signal prediction. This technique effectively ties memory and processing tasks to the intrinsic materials properties. The specific details of the physical system in which RC is implemented, however, are not relevant so long the following key criteria are met: dynamical non-linearity, high phase space dimensionality, uniquely reproducible initial state, easy out-of-equilibrium perturbation, and readability of dynamical state. The main quest is to identify a system suitable for the task, which is not plagued by real world-incompatible requirements. Our proposed solution is based on driven spin-wave excitations which guarantee both sufficiently complex transient responses, controlled chaoticity, as well as providing a natural spintronic platform for straightforward driving and reading of dynamical magnetic states. Our proposed work aims at demonstrating the versatility of spin-wave interference as the key candidate for the implementation of RC in a real-world device. We believe that spin-waves in magnetic nanostructures are ideal candidates for developing drop-in substitutes for circuit components, as well as stand-alone devices. Success in this endeavour would prove groundbreaking for the development of real-time pattern detection technologies with the potential for high-impact deployment in areas ranging from medical monitoring to climate modelling. Complex pattern recognition tasks could be performed on RC hardware with square-micrometre surface area, 100 micro-W power consumption and 10 ns inference time. Compared to the server stacks currently used by industry leaders (Google, Apple, Facebook, etc.) to satisfy global demand, success in this action will pave the way for massively more resource efficient big-data solutions.

Nanomaterials provide new opportunities for the conversion of heat into other forms of energy as they can sustain much larger temperature gradients than macroscopic systems, hence producing much stronger non equilibrium effects. These non equilibrium effects can be exploited in the generation of electricity from waste heat, thermoelectricity, one of the most important non equilibrium phenomena associated to temperature gradients, which has enormous practical implications in energy conversion. We have recently reported a novel non equilibrium effect in water, thermo-molecular polarization, where the thermal reorientation of the molecules under temperature gradients leads to sizeable electrostatic fields. This is a novel concept that can provide the basis to design and make new molecular-based devices for energy conversion. Nanomaterials offer many possibilities to exploit this novel effect, but at the same time many challenges, as it is necessary to manage heat dissipation at very small scales. Heat dissipation is a very generic problem, featuring in many different disciplines: biology (molecular motors), physics, chemistry, engineering (chemical reactions at surfaces, microelectronic devices, condensation-evaporation processes) and medical applications ('nanoheaters' for thermal therapy treatments). Energy dissipation in proteins and in particular biological molecular motors has been optimised through a long evolution process. There are lessons we can learn by investigating heat dissipation in such structures, and hence, use them as a template for new biomimetic approaches to make nanomaterials. Realising this objective requires developing appropriate tools to quantify heat transfer in nanoscale materials and biomolecules. One advantage of working at the scales characteristic of nanomaterials is that very large gradients can be achieved with temperature differences of a few degrees. These gradients are strong enough to cause local phase transformations in solids, and even destroy biological cells, a notion that is being exploited in cancer therapies. We have shown that gradients of this magnitude can induce strong polarization effects in polar fluids, of the order of the electrostatic fields needed to operate liquid crystal displays. Hence, the combination of nanomaterials and thermo-molecular effects offers an exciting principle to design novel energy conversion approaches. The investigation of these small materials is not trivial though, since they are small and intricate, making them a difficult target for experimental probes. The limited capability of experimental methods to measure the dependence of thermal transport with size and chemical composition in nanoscale materials limits our ability to develop models and hence design materials that can be exploited in energy conversion devices. Indeed, our understanding of the mechanisms controlling heat transport at the nanoscale is still scarce, but there is evidence that their description requires a molecular approach. In spite of the great advances over the past years in our understanding of heat transport in nanomaterials, there are many challenges to tackle in the near future. In recent work, new and exciting non-equilibrum effects have been reported, showing there is room to explore new principles and possibly exploit them to design energy conversion devices. In the present project we will develop new computational/theoretical approaches to investigate heat transport in nanoscale materials and biomolecules. This methodology will enable us to investigate heat flow at an unprecedented level of detail. This will make possible the development of the microscopic background needed to make the necessary breakthroughs to realise the potential of thermo-molecular effects in new and transformative energy conversion technologies.

The lead zirconate titanate (Pb(Zr,Ti)O3) system displays a fascinating range of structures and behaviours, but with the common feature that all compositions contain exhibit permanent electric polarisation at room temperature as a result of antiparallel displacements of the oxygen anions and the metal cations. At the PbZrO3 end of the composition range, the electric dipoles are arranged in stripes of antiparallel polarisation resulting in zero net polarisation, this is referred to as an antiferroelectric state. In contrast to this, for Pb(Zr[0.9],Ti[0.1])O3, polarisation all lies along the same direction resulting in a finite permanent macroscopic polarisation - a ferroelectric state. Just doping this latter composition with 2-4% La puts this into a slightly confused state, very much on the edge between ferroelectric and antiferroelectric ordering. Whilst it is well known that the crystal structure for this state has a large unit cell, which is incommensurate (i.e. it doesn't quite stack up as being made of a simple whole number of atomic stackings), the details of this structure are not at all well understood. The reasons for this are straightforward: it is big and not perfectly ordered (previous studies show frequent deviations from perfect order) and thus techniques like diffraction with X-rays or neutrons will have difficulties. Whilst some information can be inferred from conventional electron microscopy and diffraction (which has already been done by the applicant), the most straightforward way to solve the structure would be to be able to see where all the atoms are. This is now possible due to advances in aberration corrected electron microscopy. Recent developments have made it possible to compensate for the imperfections present in all electromagnetic lenses and this now allows us to resolve objects well below 1 + - a suitable scale for resolving atoms. The project partners at Jlich are world leaders in applying this to materials and have particular experience with doing such studies on perovskite oxides and in measuring electrical polarisation from imaging the oxygen and the metal cations in these structures. This project will allow the applicant with his prior experience of incommensurate antiferroelectrics to travel to Jlich and collaborate with them on imaging these fascinating materials at sub-+ngstrm resolution and in combination with data processing and image simulation to enable us to be able to determine the oxygen and cation displacements across the unit cell. As well as solving the structure of this interesting phase, it will also enable us to better understand its relationship to both the ideal antiferroelectric phase of PbZrO3 and to the rhombohedral ferroelectric phase of Pb(Zr[0.9],Ti[0.1])O3, and will prepare the ground for future studies of field induced transformations between antiferroelectric and ferroelectric phases.

Psilocybin is a compound found in 'magic mushrooms' that alters conscious perception. Our goal is to understand how psilocybin changes brain connectivity and conscious perception. To achieve this, we will bring together experts in brain anatomy and function with experts in artificial intelligence. We will first analyse and combine data on the brain's anatomy and function. We will use this data to simulate the effects of psilocybin on the brain's circuits. We can then analyse the simulation to learn how psilocybin changes brain activity and the conscious experience. Psilocybin has exploded in popularity as a recreational drug, a mental health treatment and an aid for creativity. The popularity derives in part from its effects on conscious perception. Conscious perception emerges from activity across scales of brain organisation. We aim to integrate across these scales. In this way, we will learn how the local effects of psilocybin lead to reorganisation of connections across the brain. The nature of the psilocybin experience does not only depend on biological factors. The mental state of the person taking the drug also affects the experience. We need to know how mental states can change psilocybin's effects on neural circuits. This would show how psychological and biological factors influence each other and determine our experience. Both natural chemical transmitters and drugs bind with receptors to open channels in neurons, much as a key fits a lock to open a door. We recently found the receptors that psilocybin binds to in different amounts across different brain areas. We aim to test whether this can explain the powerful influence of psilocybin on conscious perception. We propose to use a new advanced recording technology to measure psilocybin's effects on the activity of hundreds of neurons in the rat brain. We will combine this data with our maps of the locations of receptors in the brain. We will use this data to build a simulation of psilocybin's effects on the brain based on real biology. This will provide a missing link between the psychological and biological effects of psilocybin. Scientists can use this to design and refine future experiments. We have three key objectives: 1. To analyse recordings of neural activity from the rat brain to identify how psilocybin's changes brain architecture. 2. To simulate how the mental state can combine with biological factors and alter the psilocybin experience 3. To simulate how the brain's physiology and anatomy determine psilocybin's ability to alter conscious perception. This project will create an open software platform to advance our understanding of psychedelic drugs. This has the potential to move forward both our fundamental understanding of the brain and drug development.

Biomass burning (BB) and wildfires release huge quantities of particulates and trace gases into the atmosphere in amounts highly variable in space and time. Plume rise means these that under certain conditions these emissions can be injected into the atmosphere at heights far above the Earth surface, enhancing their long-range transport and altering their atmospheric chemistry, radiative budget, and air quality effects. Results from past project show that UK air quality can be signficantly affected by long-range transport of smoke from European and Russian wildires, and smoke from fires in Canada can be detected in air samples at DEFRA monitoring stations in e.g. Mace Head. Near real-time (NRT) atmospheric modelling and forecasting schemes aiming to realistically represent these aspects of the Earth system must include a high temporal resolution, non-retrospective source of BB emissions information - which generally comes from satellite Earth Obervation data. However, as discussed above, a fires smoke plumes buoyancy characteristics can strongly influence its atmospheric impact, and this is increasingly realised to be an important term to represent when modelling the long-range effects of wildfire smoke emissions. However, a lack of a priori information and, until recently, a directly-related EO observable, has meant that parameterisation of smoke plume injection height has received far less attention than has estimating the magnitude and variability of the smoke emissions. This KE Project will exploit the findings from two successful NERC research projects to provide major improvements to the current (ad hoc) prescription of wildfire smoke plume injection height in the prototype GMES UK/European atmospheric monitoring and forecasting scheme (the 'GMES Atmospheric Core Service', which is based on the world-leading integrated forecast system (IFS) of ECMWF in the UK and which is being desiged to provide the public, policy makers and downstream organisations with access to state-of-the-art atmospheric chemistry monitoring and forecasting data. The GACS serves a broad community of users, for example those involved in environmental policy development and policing, those delivering downstream services related to the health community (warning of increased asthma incidence during air pollution episodes), and those aiming to reduce public exposure to air pollution. We will work with Project Partners developing the GACS to exploit the research on plume height rise developed in NE/E016863/1 and the EO data processing procedures developed in NE/H00419X/1 to provide a much more realistic representation of smoke injection height in the GACS system; one that takes account of both fire and atmospheric characteristics such that the atmospheric transport of these emissions, including to the UK, can be better represented. The Project Partners are ECMWF, who lead GACS development in the UK and who operate the global model within which the plume rise scheme will be embedded, and Jülich Research Centre who are experts in the chemistry and transport of smoke emissions and who are a main partner in the GACS development. The KCL Environmental Research Group (KCL-ERG) are a 'down-stream' user of global atmospheric model output, funded by UK Government to provide regional air quality (AQ) monitoring and modelling, and this KE project will support them in starting to use the enhanced GACS outputs in their UK regional and London-wide AQ modelling schemes, in particular to take better take account of smoke-polluted air that is known to move into the UK from e.g. eastern Europe or western Russia, and which at present causes enhanced discrepancies between the AQ models and measurements (see DEFRA letter of support). All model outputs incorporating the new scheme will be made available freely through the GMES GACS system interface http://www.gmes-atmosphere.eu/ and for the UK region throught the online public interface www.londonair.org.uk/