Advanced materials lie at the heart of a huge number of key modern technologies, from aerospace and automotive industries, to semiconductors through to surgical implants. The transmission electron microscope (TEM) is a key enabling technology for advanced material research because it offers two important pieces of atomic information: firstly the location of atoms can be determined from studies of elastic scattering of electrons by the sample, and secondly the chemical composition of atomic sites within the materials structure can be recovered from spectroscopic studies of the inelastic transfer of energy to the sample (either from direct energy loss or by the detection of characteristic X-rays). These two pieces of information underpin a huge research area exploring the relationship between materials microscopic structure and the macroscopic properties it exhibits. With the drive towards nanotechnologies and quantum devices the ability to discover the most precise understanding of individual atoms is an essential capability for facilities supporting research of advanced materials. The aim of the project is to develop, for the first time, an analytical TEM that not only offers cutting edge spectroscopy performance but which also is designed with artificial intelligence and automated workflows at its core. The first goal will be achieved through the incorporation of the latest generation of X-ray detectors and spectrometers to provide order of magnitude improvements in chemical sensitivity and precision. This capability is essential for the move to studying devices as small as a single atomic defect as well as for efficient analysis of large areas at atomic resolution. To achieve artificial intelligence (AI)-assisted experiments the project will tackle a number of technical challenges: i. Computer control of the TEM will be developed, allowing the computer to automatically adjust the sample stage and beam to address specific regions of interest and perform auto-tuning the experimental parameters to achieve detailed high resolution imaging and diffraction based analysis of nanometric regions without the need for continuous operator interaction. ii. The mechanism to identify regions of interest will utilise the full range of machine learning (ML) approaches to segment lower resolution data, which might come from fast large-area scanning in the TEM or be the result of ex-situ analysis by optical imaging, scanning probe microscopies, scanning electron microscopy or optical approaches to name but a few. iii AI training will allow the microscope control computer to build functional relationships between experimental results in the same way a human operator does, allowing faster and more systematic identification of novel features. Our proposed new smart automated TEM (AutomaTEM) offers the opportunity to gain at least an order of magnitude increase in the volume of data that is readily accessible through automated workflow analysis. Features of interest will be determined either through user-defined parameters or through the AI identification of significant features in the collective data. This will allow meaningful statistics to be gathered about the size, shape, atomic structure, composition, electronic behaviour of potentially hundreds or thousands of regions in a given sample. This in turn will enable more complete understanding of nanostructure heterogeneity and structure-property relationships in materials.

Scientific breakthroughs are strongly associated with technological developments, which enable the measurement of matter to an increased level of detail. A prime example of this is the development of femtosecond lasers, which opened up the field of ultrafast spectroscopy. This had a huge impact on our understanding of chemical reactions, biological functions and phase transitions in materials owing to their ability to probe, in real-time, the nuclear motion within these different types of systems. A modern revolution is underway in X-ray science with the emergence of tools capable of delivering high-brilliance ultrashort pulses of X-rays. The UK, through the Diamond Light source, investment into the European X-FEL and world-leading research groups are at the forefront of these experimental endeavors. Crucially, the complicated nature and high information context of X-ray spectroscopic observables means that a strong synergy between experiment and theory is required. Since 2019, the COllaborative NEtwork for X-ray Spectroscopy (CONEXS, EP/S022058/1) has established a strong community of over 600 researchers in the area of X-ray spectroscopy, with a primary focus of nurturing a strong synergy between experiment and theory. Through providing access to state-of-the-art computing facilities, the UK High-End Computing Consortium for X-ray Spectroscopy (HPC-CONEXS) will develop computational tools to advance the detailed analysis of experimental data. It will also provide resources and training for both experts and non-experts to further enhance the synergy between experiment and theory ensuring maximum impact from the UK's research and investment in this area.

This application represents a joint bid from Newcastle, Northumbria, and Durham Universities to acquire a state-of-the-art cryogenic transmission electron microscope. Cryo-electron microscopy (cryo-EM) is a powerful imaging method that has revolutionised structural biology, with rapid technology development over the last decade enabling the visualisation of dynamic and flexible biomacromolecules and assemblies. Currently, there is no cryo-EM instrumentation in the North East of England, despite the region having research strengths and expertise in structural biology across the lead institutions. Our vision is to future-proof these North East institutions in next-generation cryo-EM. This proposal will equip the region with an indispensable tool to study dynamic macromolecular complexes that are not readily amenable to X-ray crystallography. The proposed instrument is a new class of cryo-electron microscope that: enables timely and efficient sample optimisation through real-time screening and preliminary analysis, thereby addressing a key bottleneck in the cryo-EM workflow; is a user-friendly instrument for training structural biologists in cryo-EM, a highly valued skill set in academic and industrial careers; is a cost-effective platform for building cryo-EM capability and expertise at the institution level. The instrument will be housed within the Newcastle University Structural Biology Facility, that is already equipped with a suite of X-ray crystallography and IT resources. This Facility will therefore become a hub for North East researchers to pursue and collaborate on biomacromolecular structure determination for both fundamental and translational research. The instrument will be managed through an organisational structure of operational, strategic, and user groups, with representation from all three institutions, to ensure effective day-to-day operation and decision making. The instrument user-base composes some 30 research groups from the three lead institutions, with a strong track record in studying diverse biomacromolecules across the kingdoms of life. The team of applicants also includes technology specialists with expertise in different applications of electron microscopy, as well as early-career researchers building their structural biology research portfolios. The instrument will enable research to advance the frontiers of molecular biosciences across the BBSRC strategic challenges of sustainable agriculture and food, renewable resources, and integrated understanding of human health. We therefore request funds for the purchase of a 100 kV cryo-EM instrument with direct electron detector. While we will conduct a WTO compliant tender process, a microscope in this class is currently only available from ThermoFisher Scientific. We have engaged with them to test the microscope with our samples, undertaken site survey to develop a delivery and installation plan, and have secured a highly favourable discount and service package. The cross-institution partnership makes a clear commitment to build complementary strengths in bioscience, biomedicine, and biotechnology; to attract researchers and resources to the North East; and to place the institutions at the forefront of structural biology. As endorsement of this commitment, the three institutions have committed to provide matched contribution totalling 30% of the equipment cost, as joint capital investment.

Artificial intelligence (AI) is undergoing an era of explosive growth. With increasingly capable AI agents such as chatGPT, AlphaFold, Gato and DALL-E capturing the public imagination, the potential impact of AI on modern society is becoming ever clearer for all to see. APRIL is a project that seeks to bring the benefits of AI to the electronics industry of the UK. Specifically, we aspire developing AI tools for cutting development times for everything from new, fundamental materials for electronic devices to complicated microchip designs and system architectures, leading to faster, cheaper, greener and overall, more power-efficient electronics. Imagine a future where extremely complex and intricate material structures, far more complex than what a human could design alone, are optimised by powerful algorithms (such as an AlphaFold for semiconductor materials). Or consider intelligent machines with domain-specialist knowledge (think of a Gato-like system trained on exactly the right milieu of skills) experimenting day and night with manufacturing techniques to build the perfect electronic components. Or yet what if we had algorithms trained to design circuits by interacting with an engineer in natural language (like a chatGPT with specialist knowledge)? Similar comments could be made about systems that would take care of the most tedious bits of testing and verifying increasingly complex systems such as mobile phone chipsets or aircraft avionics software, or indeed for modelling and simulating electronics (both potentially achievable by using semi-automated AI coders such as Google's "PaLM" model). This is precisely the cocktail of technologies that APRIL seeks to develop. In this future, AI - with its capabilities of finding relevant information, performing simple tasks when instructed to do so and its incredible speed - would operate under the supervision of experienced engineers for assisting them in creating electronics suited to an ever-increasing palette of requirements, from low-power systems to chips manufactured to be recyclable to ultra-secure systems for handling the most sensitive and private data. To achieve this, APRIL brings together a large consortium of universities, industry and government bodies, working together to develop: i) the new technologies of the future, ii) the tools that will make these technologies a reality and very importantly, iii) the people with the necessary skills (for building as well as using such new tools) to ensure that the UK remains a capable and technologically advanced player in the global electronics industry.