This project will use machine learning approaches to extract physico-chemical information from chemical imaging data. This novel approach will tackle an emerging problem in this field, namely how to automatically identify and extract chemical signals from the rich and ever-larger datasets that it is now possible to collect. There are several features that suggest this problem can be tackled using machine learning approaches. We have developed software for the rapid simulation of chemical imaging data, and we can use this to generate large labelled datasets for training the convolutional neural networks (CNN) that we will build. In addition we have substantial libraries of real data which the developed CNN's can be tested against.

Our company has developed advanced chemical imaging capabilities which we offer as a service to industry, helping our clients accelerate their R&D. Our imaging approaches yield rich and large datasets that contain an abundance of physico-chemical information. This project will use artificial intelligence approaches to reconstruct X-ray scatter-based chemical tomography data from large objects.Large objects pose a problem due to geometric blurring of the scattered signals on the receiving detector, preventing conventional reconstruction approaches. We have spent considerable resources developing a non-linear least-squares algorithm to address this but it is computationally demanding and because of this imposes resolution limits on the reconstructed data (i.e. small images size). We have realised though that the problem has several features which indicate that it can be tackled by using deep learning approaches. Additionally, we have the ability to generate very large simulated labelled datasets that can be used as training sets for supervised learning using convolutional neural networks (CNNs). This is in addition the very large real data sets we have at our disposal. Whilst there are existing attempts to reconstruct conventional tomography data using CNNs, we are planning to develop new CNNs for reconstructing chemical (hyperspectral) tomography data and indeed overcome the parallax problem. The project thus is innovative both in terms of approach and application and will push the opportunities in this emerging field.

no public description

STORMING will develop breakthrough and innovative structured reactors heated using renewable electricity, to convert fossil and renewable CH4 into CO2-free H2 and highly valuable carbon nanomaterials for battery applications. More specifically, innovative Fe based catalysts, highly active and easily regenerable by waste-free processes, will be developed through a smart rational catalyst design protocol, which combines theoretical (Density Functional Theory and Molecular Dynamics Calculations) and experimental (cluster) studies, all of them assisted by in situ & operando characterisation and Machine Learning tools. The electrification (microwave or joule-heated) of structured reactors, designed by Computational Fluid Dynamics and prepared by 3D printing, will enable an accurate thermal control resulting in high energy efficiency. The project will validate, at TRL 5, the most promising catalytic technology (chosen considering technological, economic, and environmental assessments) to produce H2 with energy efficiency (> 60%), net-zero emissions, and decreasing (ca. 10 %) the costs in comparison with the conventional process. The dissemination and communication of the results will boost the social acceptance of the H2-related technologies and the stakeholder engagement targeting short-term process exploitation and deployment. The key to reach the challenging objectives of STORMING is the highly complementary and interdisciplinary consortium, where basic and applied science merge with engineering, computer and social sciences.

The Centre for Doctoral Training in "Molecular Modelling and Materials Science" (M3S CDT) at University College London (UCL) will deliver to its students a comprehensive and integrated training programme in computational and experimental materials science to produce skilled researchers with experience and appreciation of industrially important applications. As structural and physico-chemical processes at the molecular level largely determine the macroscopic properties of any material, quantitative research into this nano-scale behaviour is crucially important to the design and engineering of complex functional materials. The M3S CDT offers a highly multi-disciplinary 4-year doctoral programme, which works in partnership with a large base of industrial and external sponsors on a variety of projects. The four main research themes within the Centre are 1) Energy Materials; 2) Catalysis; 3) Healthcare Materials; and 4) 'Smart' Nano-Materials, which will be underpinned by an extensive training and research programme in (i) Software Development together with the Hartree Centre, Daresbury, and (ii) Materials Characterisation techniques, employing Central Facilities in partnership with ISIS and Diamond. Students at the M3S CDT follow a tailor-made taught programme of specialist technical courses, professionally accredited project management courses and generic skills training, which ensures that whatever their first degree, on completion all students will have obtained thorough technical schooling, training in innovation and entrepreneurship and managerial and transferable skills, as well as a challenging doctoral research degree. Spending >50% of their time on site with external sponsors, the students gain first-hand experience of the demanding research environment of a competitive industry or (inter)national lab. The global and national importance of an integrated computational and experimental approach to the Materials Sciences, as promoted by our Centre, has been highlighted in a number of policy documents, including the US Materials Genome Initiative and European Science Foundation's Materials Science and Engineering Expert Committee position paper on Computational Techniques, Methods and Materials Design. Materials Science research in the UK plays a key role within all of the 8 Future Technologies, identified by Science Minister David Willetts to help the UK acquire long-term sustainable economic growth. Materials research in UCL is particularly well developed, with a thriving Centre for Materials Research, a Materials Chemistry Centre and a new Centre for Materials Discovery (2013) with a remit to build close research links with the Catalysis Technology Hub at the Harwell Research Complex and the prestigious Francis Crick Institute for biomedical research (opening in 2015). The M3S will work closely with these centres and its academic and industrial supervisors are already heavily involved with and/or located at the Harwell Research Complex, whereas a number of recent joint appointments with the Francis Crick Institute will boost the M3S's already strong link with biomedicine. Moreover, UCL has perhaps the largest concentration of computational materials scientists in the UK, if not the world, who interact through the London-wide Thomas Young Centre for the Theory and Simulation of Materials. As such, UCL has a large team of well over 100 research-active academic staff available to supervise research projects, ensuring that all external partners can team up with an academic in a relevant research field to form a supervisory team to work with the Centre students. The success of the existing M3S CDT and the obvious potential to widen its research remit and industrial partnerships into topical new materials science areas, which lie at the heart of EPSRC's strategic funding priorities and address national skills gaps, has led to this proposal for the funding of 5 annual student cohorts in the new phase of the Centre.