See Joint Proposal E241901

Supercomputers (HPC) provides exciting opportunities for simulation-led design of materials and processes. Our project builds on the expertise in the UK Materials Chemistry Consortium, to exploit world-leading supercomputers with a programme of research into the behaviour of the materials used in applications and devices including thin-film solar cells, high-capacity batteries, flexible electronic displays, hosts for toxic waste products, biomaterials with medical applications, and cheaper and more efficient production of green fuels and of bulk and fine chemicals from detergents to medicines, thus transforming society and people's lives. The project comprises application-driven and cross-cutting themes focused on fundamental challenges in contemporary materials chemistry and physics and advanced methodology. It brings together the UK's materials academic community, currently representing 38 universities. Close interaction will promote rapid progress, novel solutions, and best practice resulting in both applied and fundamental developments. Our work will be guided by an advisory panel of leading international academics and industrial experts and collaborators. Our goal is to maintain a vigorous scientific endeavour within the current membership and in doing so attract likeminded professionals and non-traditional HPC users. Tuning properties of materials forms the backbone of research in Energy Conversion, Storage and Transport, a key application theme for the UK's economy and net-zero targets. We will aim to improve the performance of materials used in both batteries and fuel cells, as well as novel types of solar cells. In Reactivity and Catalysis, we will develop realistic models of several key catalytic systems. Targets relate strongly to the circular economy and include CO2 activation and utilisation, green ammonia production, biomass conversion and enhancement of efficiency in industrial processes and more effective reduction in air pollution. We will develop environment protecting materials to contain toxic and/or radioactive waste, capture greenhouse gases for long-term storage, remove toxins and pollutants from the biosphere to improve wildlife and human health, and control transmission of solar energy through windows. Work on Biomaterials will reveal the fundamental processes of biomineralisation, which drives bone repair and bone grafting, with a focus on synthetic bone replacement materials. Materials Discovery will support screening materials using global-optimisation-based approaches to develop tailored chemical dopants, improving the desired property of a device, and searching the phase diagram for solid phases of a pharmaceutical drug molecule. Crosscutting themes will focus on basic issues in the physics and chemistry of matter that underlie the application themes. They will address: challenges in predicting the morphology, atomic structure and stability of different phases; defects and their role in material growth, corrosion and dissolution in Bulk, Surfaces and Interfaces, and at Nano- and meso-scales. Our simulations will investigate materials far from equilibrium, as well as quantum and nano-materials with links to topological spintronics. Software developments will include utilising machine learnt potentials, significantly increasing the time- and length-scales of simulations (compared to electronic structure-based calculations) without compromising their accuracy and predictive power. We will continue to develop new functionalities and optimise performance of internationally leading materials software and link to research exploiting quantum computers. We will continue training postgraduate students and researchers in the use of HPC resources and application of scientific software to materials problems. As experts, we will continue to perform the crucial knowledge transfer providing expertise to the UK society from the school level up to the Government funding agencies.

Historically the optical fibre was perceived to provide "unlimited" bandwidth, however, the capacity of current communications systems based on single mode optical fibre technology is very close to the limits (within a factor of 2) imposed by the physical transmission properties of single mode fibres. The major challenge facing optical communication systems is to increase the transmission capacity in order to meet the growing demand (40% increase year-on-year) whilst reducing the cost and energy consumption per bit transmitted. If new technologies are not developed to overcome the capacity limitations inherent in single mode fibres and unlock the fibre bandwidth then the growth in the digital services, applications and the economy that these drive is likely to be curtailed. The need for increased capacity in the core and metro areas of the network and within computing data centres is likely to become even more acute as optical access technologies, providing far greater bandwidths directly to the users, take hold and services such as ubiquitous cloud computing are adopted. Multimode optical fibres (MMF) offer the potential to increase the capacity beyond that of current technologies by exploiting the spatial modes of the MMF as additional transmission paths. To fully exploit this available capacity it is necessary to use coherent optical (CO) reception and multiple-input multiple-output (MIMO) digital signal processing techniques analogous to those already used in wireless communication systems such as WiFi. This project aims to develop the technologies and sub-systems required to implement a CO-MIMO system over MMF that exceeds the capacity of current single mode fibre systems and reduces the cost and energy consumption per bit transmitted. To achieve this goal the project addresses the following key engineering challenges necessary to realise a complete system demonstrator. Engineer the channel: The multimode optical fibre MIMO channel, unlike its wireless counterpart, presents the opportunity to engineer the optical channel to optimise its performance for MIMO operation by designing and fabricating new optical fibres, using proven solid core technology, to maximise the MIMO capacity of the fibre. Dynamically control the channel: The transmission characteristic of the multimode optical fibre channel varies with time. We will exploit both the flexible and fast adaptive nature of digital signal processing, and the less energy intensive and slower adaptation of liquid crystal spatial light modulator based optical signal processing to compensate for the channel variation and recover the spatially multiplexed data channels. Employ energy efficient optical amplification: In order to reduce both the energy consumption and cost per bit and to extend the propagation distance into the hundreds of kilometres region it is essential to develop optical fibre amplification technologies that provide amplification to multiple spatial and wavelength channels and thus share the cost. Coherently detect the optical signal to exploit the wavelength and spatial domains: The developed system will combine spatial multiplexing with existing dense wavelength division multiplexing, polarisation multiplexing and multilevel modulation techniques to maximise the capacity. The key to achieving this is the use of coherent optical detection and digital signal processing, which is essential not only to fully exploit the spatial capacity of the MMF channel, but also facilitates the use of existing multiplexing techniques that are difficult to realise in conventional multimode transmission systems. The technologies and systems developed within this project will find applications, ranging from capacity upgrades of existing MMF data networks in data and computer processing centres, through to the installation of new high capacity metro and long haul fibre transmission systems using the MIMO optimised fibres and technologies developed in this project.

The semiconductor industry has provided the devices we have enjoyed for many years, including mobile phones, personal computers, on-line banking etc. The growing functionality of these products is a result of making the components, namely transistors and memory elements, ever smaller, at the rate that in every 18 months or so the number of components in a given area has doubled, which also makes the devices run faster. The industry now runs into a fundamental roadblock in shrinking the devices further, so we need to look for a new device type which will continue to provide higher performance. One strong contender is the RRAM (resistive random access memory) which we will investigate in this project. This device can be programmed to offer either a high or low electrical resistance: that is, store a logic "0" or "1", or even with some intermediate levels in between. It can store information which will remain even after the power is turned off, as so called non-volatile. With this device, a number of disruptive developments are under intensive research world-wide. Its first potential application is to increase the speed of the non-volatile memory chip in computers by more than 10 times and provide potential for further increase in the number of components. The second is in the artificial intelligence (AI) computing which mimics the functionality of human brains. AI has been widely used by Google, Facebook, Apple, etc. RRAM has the potential to bring a breakthrough in AI by solving the density, connectivity and memory bandwidth limitations of AI hardware based on conventional devices. The third is to revolutionise the programmable computing with its smaller size and non-volatility, providing advantages for computing in data centres and Internet of Things, in which the vast amount of data will be streamed through internet and the scalability and energy efficiency provided by RRAM become critical. The behaviour of RRAM devices, however, is stochastic, meaning that a large variation occurs during the device operation. At present, the lack of systematic understanding of the variability and the missing tools for variability-aware simulation hinder the research progress in RRAM-based circuit and systems design for neuromorphic and programmable computing. In this project we will collaborate with UK's leading IC design company, ARM Holdings, and the world no.1 EDA software company, Synopsys, providing direct insight into the fundamental properties of RRAM variability and developing a predictive variability-aware product design kit (PDK) that can be directly used within commercial EDA software by designers, enabling the research and design of novel RRAM based neuromorphic and programmable computing systems. We expect this project to have a significant direct impact on the UK and global ICT industry in the forthcoming Artificial Intelligence (AI) and Internet of Things (IoT) era.

Flash memories are used to store phone numbers, music, pictures and videos in mobile phones and are also frequently now used in place of magnetic hard disks in laptop computers. Such memories are non-volatile retaining information even if a battery looses all charge. Consumers constantly want more memory on their portable electronic devices to allow more video and music to be stored but flash memory is already close to the scaling limits preventing significant increases to memory sizes in the future. A flash memory consists of a floating gate charge node where the a single bit of digital information is stored as a "1" when the node is charged and "0" when the node is discharged. As the floating gate is reduced in size, there are more errors when electrons leak out of or onto the floating gate. These errors result from variation in floating gate size by just a few atomic layers which are sufficient to substantially change the applied voltage required to tunnel electrons onto or off the floating gate. This limit has been reached with present production. Our approach to improve flash memory and allow smaller memories is to use molecules which are produced chemically to allow charges to be stored as the digital memory and as the molecules are all identical, they do not suffer the same variability errors as the present silicon floating gate flash memories. Out ultimate aim is to use single molecules to enable further scaling thereby aiming to increase the amount of memory available in the future. We will also investigate molecules that can store more than "0" and "1" known as multi-valued memory. This multi-valued memory approach allows more bits to be stored on a single floating gate thereby allowing higher memory density expanding further what could be stored on a mobile phone or laptop computer. The approach we are taking requires the ability to measure the state an electron occupies on a single molecule. Therefore the technique developed here could be used to measure the properties of single molecules. This has potential applications for measuring the electronic properties of single molecules directly allowing the full characterisation of the molecular levels which at present is difficult to achieve. We believe these techniques can benefit a wide range of researchers in chemistry, physics, materials science and engineering in achieving far cheaper characterisation of materials at the nanoscale.