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Molecular simulations of biomolecular interactions and assembliesPrinciple Investigator: Jamshed Anwarhttps://www.lancaster.ac.uk/chemistry/about/people/jamshed-anwar Project DescriptionThe project aims to develop a fundamental understanding of biomolecular assemblies using molecular simulation. The interest is in self-assembly, phase transformations, interactions between assembled structures, and how assembled structures can be perturbed. Self-organised molecular aggregates play a key role in biology, pathology (disease), and nanotechnology applications. Indeed, all biological structures from biomolecular complexes, membranes, organelles, and upwards rely on molecular self-assembly. Soft-matter aggregates are also of technological interest and include micellar solutions and microemulsions, free-standing and supported lipid layers, lipid vesicles (currently being used for the delivery of mRNA-based vaccines), and block co-polymer complexes that are being exploited in molecular sensing devices. Molecular simulation offers unprecedented molecular-level resolution with the ability to reproduce bulk and molecular-level properties. It can yield important insights, inform experimental studies, and demonstrate a significant predictive potential.
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GPU-isation of the CASINO quantum Monte Carlo codePrinciple Investigator: Neil Drummondhttps://www.lancaster.ac.uk/staff/drummonn/ Project DescriptionThe structural, electronic, and optical properties of materials are largely determined by the behaviour of the electrons that bind their atoms together. The fundamental equations describing electrons in materials have been known since the 1930s, but they are extremely challenging to solve. Quantum Monte Carlo methods, such as those implemented in our code CASINO, provide brute-force, approximate numerical solutions to those equations. However, computational expense is one of the major limiting factors on what can be achieved in quantum Monte Carlo calculations. This project aims to ensure that CASINO can efficiently exploit modern computer hardware such as graphics processing units.
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Data/CulturePrinciple Investigator: Katherine McDonoughhttps://www.lancaster.ac.uk/history/about/people/katherine-mcdonough Project DescriptionData/Culture (https://www.turing.ac.uk/research/research-projects/dataculture-building-sustainable-communities-around-arts-and-humanities) is an AHRC-funded project focused on building sustainable communities around Arts and Humanities datasets and tools. Our work follows on from the Living with Machines project (https://livingwithmachines.ac.uk/) and focuses on developing tools and software to work with digitised collections of maps and newspapers in Britain. Examples of the tools we have developed include T-Res (https://github.com/Living-with-machines/T-Res) and MapReader (https://github.com/maps-as-data/MapReader).
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Re-thinking the use of VAEs in the Bayesian Optimisation of Structured SpacesPrinciple Investigator: Henry Mosshttps://www.lancaster.ac.uk/maths/people/henry-moss2 Project DescriptionScientists in expensive fields like drug discovery, materials science, and engineering can now run many experiments quickly and use powerful computers to tackle bigger challenges than ever. Generative AI—software that can create new images, molecules, or even engineered structures—could transform how experiments are planned, performed, and refined. This project is developing the key methodology needed to unlock Generative AI’s full potential in designing experiments, helping speed up innovation for both science and industry.
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Efficient Point Cloud Video ProcessingPrinciple Investigator: Hossein Rahmanihttps://www.research.lancs.ac.uk/portal/en/people/hossein-rahmani Project DescriptionWe are exploring efficient point cloud video processing methods. As 3D sensors such as RGB-D cameras and LiDARs have become more affordable and widespread, 3D point cloud video processing has become an important and hot research topic. Compared to 2D images, 3D data contains rich geometric information, which could give the model a better understanding of the scene. The Point Cloud is a commonly used 3D data format since it keeps the original geometric information of the 3D data. Therefore, it is used in many critical scene-understanding tasks such as robotic and automatic driving systems. In these applications, the model always needs to give the result or make the decision in a limited time with limited computational resources. Therefore, we need to explore efficient point cloud video processing methods for these real-world applications' requirements.
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Investigation of metal halide compounds and perovskites for energy applicationsPrinciple Investigator: Nourdine Zibouchehttps://www.lancaster.ac.uk/chemistry/about/people/nourdine-zibouche Project DescriptionMetal halide compounds have emerged as a promising new class of materials for next-generation solar cells and other emitting devices due to their unique properties. However, their performance is limited by the complex interplay between optoelectronic processes in these materials. To better understand these structure-property relationships, this project aims to use computational simulations based on quantum mechanics methods to investigate the behavior of metal halide perovskites at the atomic level in order to gain insights into how to improve their efficiency and stability. This research could lead to the development of more efficient and cost-effective solar cells, other optoelectronic energy storage devices.
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Exploring cross-modal training for low-resource language modelsPrinciple Investigator: Hossein Rahmanihttps://www.lancaster.ac.uk/sci-tech/about-us/people/hossein-rahmani Project DescriptionLow-resource languages present a challenge for NLP because most training requires large quantities of well-annotated data. Our training explores an approach to train language models for many languages on minimal data using images instead.
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Monitoring of the evaluation metrics during the task of French Financial Narrative SummarizationPrinciple Investigator: Nadhem Zmandarhttps://www.research.lancs.ac.uk/portal/en/people/nadhem-zmandar Project DescriptionThis projects Monitors the behaviour of different summarisation evaluation metrics during the task of French Financial Narrative Summarization
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