Climate change has catalysed a worldwide search for renewable and sustainable products to replace oil-derivatives. One quarter of the global greenhouse gas emissions come from the transportation and chemicals industry, and there is a strong desire to find alternative fuels and chemicals, which can be produced with environmentally friendly processes. One possibility is the use of genetically modified microorganisms grown on renewable feedstocks to produce fuels and chemicals by fermentation. Of importance is the production of branched-chain alcohols, namely isobutanol, a desirable next-generation biofuel, suitable for a multiproduct biorefinery. Isobutanol is quite toxic to the production microorganism, and depending on the concentration, it can inhibit cell growth completely, or affect cell physiology in a way that lowers the yield and total amount of isobutanol produced. Yeasts belonging to the Saccharomyces genus can hybridise readily, creating first-generation (F1) hybrids with unique phenotypes enabling them to survive and proliferate in the stressful environments, and hence can be considered as potential microbial hosts for industrial processes. Hybridisation provides a novel source of variation for evolution to act upon, leading to adaptations that could not occur in either parental species. The problem with the development of inter-specific hybrids is that, although they are viable and sometimes fitter than the parents, they are sterile, and hence not genetically tractable. An example from Aristoteles's time is the hybrid between a female horse and a male donkey, namely the mule, which is a healthy animal employed in several human activities but cannot produce any offspring. Adaptive Laboratory Evolutionary (ALE) experiments, where a microbial population is evolved under a specific pressure to select for fitter progeny, has only been carried out on specific strains or sometimes on F1 hybrids. Although the F1 hybrids contain all the genetic diversity of the two parents, there is no recombination and random segregation of alleles, as it would happen after meiosis where specific parental traits are distributed in the offspring in different combinations and can give rise to different phenotypes. In our lab, we were able to overcome hybrid sterility by duplicating the genome content of the diploid F1 hybrids, making them tetraploids. Such tetraploid lines could undergo meiosis and so we were able to create F12 progenies with a large and diverse combination of traits. Here, we propose to use ALE to evolve F12 hybrid lines and their parents, for tolerance to isobutanol. The objective is to identify specific genotypes with high tolerance to this branched-chain alcohol, and to develop new potential production hosts. Furthermore, we will demonstrate that the higher genetic diversity, engrained in the ancestral F12 population, increases the microbial adaptation potential, and leads to a larger pool of extreme phenotypes in the evolved F12 hybrids compared to the evolved parents. We plan to sequence the genome of the best candidates and to study their gene expression to identify genes, promoters, and pathways that are responsible for the isobutanol tolerance trait. We will genetically re-construct in the parental strains a selection of genetic variants, identified in the evolved F12 population that are resistant to isobutanol, to validate their phenotypic effects. Lastly, we will grow the evolved high tolerant hybrids in the presence of other toxic compounds of industrial relevance to see whether they acquired some cross-protection to other branched-chain alcohols or whether, due to the acquired adaptation to isobutanol, they lost the ability to grow efficiently on other inexpensive renewable substrates. Ultimately, the more valuable hybrids will be those that have acquired isobutanol resistance with the least trade off in other relevant industrial conditions.

The combination of computer simulation with experiment is fundamental to achieving new understanding in chemistry, and to delivering advances that can address the most pressing societal challenges. The integration of computer simulation into research across the chemical sciences has been accelerated by the accessibility of high-performance computing infrastructure and tailored software that can harness the distributed architectures. New materials and chemical processes can be predicted by models of atoms and electrons using this infrastructure, with periodic density functional theory (DFT) at the forefront of the field of applied materials simulation. However, the efficacy of these modelling paradigms is proportional to the degrees of freedom in the system, which means that big models with lots of electrons, such as when considering catalytic processes, become very expensive to simulate. To address these shortcomings, this Fellowship looks to improve the capability and accessibility of methods that can provide high-level accuracy for electronic structure simulations, necessary for bond-breaking or bond-forming reactions, with reduced degrees of freedom, which means simulations can be performed quicker. This Fellowship is delivering new multiscale modelling paradigms, and the aim of this renewal is to make these paradigms more accessible through easier to use frameworks, and to extend our capabilities by integrating new machine-learning models into the simulation workflow, with the potential for acceleration in accurately resolving aspects of the system wavefunction. The new capabilities will continue to be developed in internationally leading software packages (FHI-aims, ChemShell) with collaborative partners distributed globally in academia and government research laboratories. The Fellowship will simultaneously look to demonstrate the potential of these new methods, with aims to resolve key mechanistic aspects of the synthesis of renewable fuel in collaboration with experimental partners in academia, notably at the host institution (Cardiff Catalysis Institute, Cardiff University) and via collaborations through the UK Catalysis Hub, as well as industry (Johnson Matthey, bp). The Fellowship aims to provide new knowledge of how the catalytic active site structure defines reactivity and selectivity in processes relating to photo- and electro-catalytic H2 generation; and also to explore how the structure of support materials influences thermally driven catalytic transformation of waste to sustainable aviation fuel. Finally, the Fellowship has complementary aims to support the transition of the research team from emergent researchers to influential and authoritative research leaders who can support the development of both new research domains and the next generation of researchers. The research team will be supported in developing, practising, and reflecting on their leadership activities, so they can deliver lasting impact in their sphere of influence.

Bio-refinery has been proposed as a solution to replace oil-derived products with sustainable biotechnologies, which is to produce value-added chemicals from renewable feedstocks. Biomass conversion processes are hampered by the high costs linked to product purification and recovery, which in many cases can be as high as 50%-80% of the total production cost. The primary reason that product recovery cost is so high is because organic acid fermentation needs to be controlled at neutral pH to ensure that the fermentation microorganisms is at its optimal performance condition. When product, organic acid is produced and gradually accumulates in the fermenter, broth pH decreases and drifts immediately. Base will then be added to adjust pH, which results in formation of the organic acid salt. Given that pKa values for most organic acids of commercial interests are between 3 and 5, the use of production hosts that can produce organic acids efficiently below pH 4.0, will decrease or eliminate the formation of organic acid salts. There is therefore, a need to develop new production hosts that have an optimum pH below 4.0. Several species of filamentous fungi can naturally produce high levels of organic acids, however they are difficult to work with because of their filamentous growth, lack of genetic versatility, and the risk of potential harmful by-products such as aflatoxins. Varieties of yeast strains are known for their capability of growth under acidic conditions, and are more amenable to genetic manipulation. Saccharomyces bulderi (aka Kazachstania bulderi), isolated in anaerobic maize silage, is a Saccharomyces sensu lato yeast species with novel physiological characteristics, able to sustain efficient growth rate over a wide range of pHs between 5.0 and 2.5. Such growth characteristics are the results of specific physiological adaptations occurred in this species, making K. bulderi an excellent candidate to be developed as a new production host for low pH fermentation. The genus Kazachstania has around 63 associated species, and despite the fact is closely related to Saccharomyces only limited genetic studies and molecular tools are available. This genus is quite diversified in term of phenotypes, morphologies, genome sizes and chromosome numbers, compared to the genus Saccharomyces. Here, we propose to fully characterise the three known species of K. bulderi at genetic and genomic level. We intent to carry out whole genome sequencing, assemble the genomes into chromosomes, determine polymorphisms, ploidy and chromosomal rearrangements. This knowledge will give us the molecular starting point to understand this species and to create an array of genetic tools for its swift manipulation. Specifically we will engineer the strains to produce a proxy organic acid (i.e. lactic acid) as a proof of concept level. Data on global gene expression collected for these strains grown at high and low pH will help us to identify the key players responsible for the specific physiological adaptations to acidic environments. Hybridisation between yeast species occurs readily in natural and domesticated environments, bringing together different traits in the same genetic background. Hybrids can be resilient to specific conditions and therefore perform better in some harsh industrial environments. We intend to cross different strains and species of Kazachstania genus and assess the resulting hybrids for genome stability and mitochondria DNA inheritance (since different type of mitochondria can affect phenotype). Hybrids with improved biomass at low pH will be selected. The ultimate goal is to be able to evaluate K. bulderi as a new production host for the production of organic acids by fermentation.

The Centre for Doctoral Training in Green Industrial Futures (CDT-GIF) will deliver the next generation of global leaders in the energy transition, through a world-leading, interdisciplinary whole systems research and training programme to address national and global priorities to realise the green industrial revolution. The CDT-GIF is critically important, as skill shortages are currently limiting the opportunities of the green industrial revolution, adding significant risk of loss of economic and social value. For example, over 350,000 additional jobs (28% professional roles) are required to meet the demands of the current UK industrial cluster decarbonisation projects between 2025 to 2040. Therefore, there is a substantial and pressing demand for training doctoral-level graduates to fill these roles to drive R&D for industrial decarbonisation, lead critical important decarbonisation projects, and prepare future graduates for the net zero agenda. The CDT-GIF directly addresses this and is in closed alignment with the EPSRC mission inspired priority 'Engineering Net Zero' by providing an industry-guided, interdisciplinary training environment in transformative low-carbon technologies that will uniquely train 100 doctoral students, whilst leveraging significant investment from academic and industry partners. Four institutions with global standing in decarbonisation (Heriot-Watt University, Imperial College London, University of Bath and University of Sheffield) have partnered with a comprehensive range of stakeholders to ascertain the critically in-demand skills and knowledge that prospective employers are seeking to deliver net zero industries. These include technically trained on systems thinking, career ready and industry literate, and internationally connected. As a result, we have co-developed a training programme, based on three pillars, that will equip our students with these attributes, namely: (1) a cohort-based whole systems taught training programme (2) metaskills development programme (Net Zero Leadership Programme), and (3) unrivalled international opportunities to visit world-leading facilities, e.g. National Carbon Capture Centre (USA), ECCSEL (European network), Heriot-Watt Dubai campus and UNECE Sustainability Week. The training elements of the programme will run parallel to student's research in order to ensure cohesive learning within and across yearly cohorts, building peer-to-peer networks. A series of activities have been designed to foster a cohesive cohort trained in a diverse and inclusive environment that engenders a culture of environmental sustainability, research trust and responsible research and innovation. The CDT-GIF research and training programme is centred on four technological themes, with one cross-cutting systems theme: (1) Advancing carbon capture, utilisation and storage technologies, (2) Green hydrogen & low carbon fuels, (3) Developing next generation CO2 removal technology, (4) Energy processes, systems integration & resource efficiency, and (5) Integrated thematic areas including socio-behavioural change, policy & regulation and net zero economics related to the four technological themes. Within these themes, students will undertake challenging & original research projects that will be co-created with industrial collaborators to discover transformative, responsible and integrated solutions to achieve net zero. Challenging and original research projects will be rooted in one of these research themes, as well as across three integrated thematic areas and supervised by >75 internationally recognised researchers with excellent track record of doctoral supervision. In summary, CDT-GIF has the capacity, expertise and unique training opportunities to deliver the most comprehensive and transformational Centre for Doctoral Training to realise the green industrial revolution.

Chemistry impacts most areas of our lives, including healthcare, energy production, and the environment. It is also the UK's second largest manufacturing industry, employing 140,00 people. This hub will bring the transformative power of artificial intelligence (AI) to the area of chemistry, and by doing so have a major societal impact. Both AI and chemistry are fast-moving and historically separated research disciplines, and there is huge untapped potential to collaborate at the interface of these two fields. Today, relatively few UK experimental chemists are exploiting AI (e.g., for reaction optimization), and few have corresponding automation facilities to do this, which is a missed opportunity. The use of machine learning methods is more common in computational chemistry, but here also we are often data poor, and data is sparse. In some AI fields, such as natural language processing, there is also rapidly evolving, leading-edge industrial research, necessitating a cross-sector approach if we are to exploit the cutting edge of this technology. This hub (AIchemy) will bring together leading researchers in AI and trailblazers at the interface of AI for chemistry, spanning both university and industry. We will exploit unique established facilities and institutes in the four core partner institutions (Universities of Liverpool, Imperial, Cambridge, and Southampton) where cross-discipline working has already been achieved: this includes the Materials Innovation Factory (MIF), the Institute for Digital Molecular Design and Fabrication (DigiFAB), and the I-X Centre for AI in Science. In addition to the 6 lead investigators, we have aligned 25 other investigators across nine institutions, spanning the areas of AI, robotics, and a diverse range of experimental and computational chemistry sub-disciplines, and career stages. The team also includes unique expertise in robotics and automation (Liverpool & Imperial), natural language processing for chemistry problems (Cambridge) and data curation in the Physical Sciences Data Infrastructure (PSDI, Southampton). This diverse team and associated facilities give us the breadth of expertise and critical mass to become the core of a UK hub for this activity. AIchemy will carry out world-leading research at the AI/chemistry interface, building on distinctive UK strengths in this area and developed initially via 6 Forerunner Projects. The Hub will also build an approach for sharing chemistry research data and code in a common format to unite the currently fragmented UK research landscape. We also aim to dramatically broaden the number of AI researchers tackling chemistry problems, and vice versa, through a mixture of pump-priming funding in the hub, bespoke training, access to datasets, and events (e.g., AI challenges using hub-generated data). To ensure the long-term health of this discipline, we will also focus resource on projects that are led by early career academics. The hub will build a UK-wide consortium involving university and industry stakeholders outside of the core partners, including a broad set of 15 day-one industry partners across the sectors of AI and chemistry, to be further expanded in the full proposal. The team has an excellent collective track record in industry engagement and knowledge transfer; e.g. MIF collocates 100 industry researchers in a common facility with academics; Chemistry is co-located with IX at Imperial's £2 Bn White City campus, and there are shared spaces to enable 800 scientists and industry partners to work together on common challenges, with tailor-made labs and offices for early stage companies. Mirroring the enormous benefits that have been achieved in other science areas, such as structural biology, this hub will transform the UK landscape for the discipline of chemistry, transforming engagement with AI from a relatively niche activity to a core, platform methodology.