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Quantum: Projects in progress


Projects in Progress

Project Title

Description

Research Student Supervisor(s)
2D Material Heterostructures and novel Twistronic Devices This project explores the exciting and novel physics of multilayer structures built from 2D materials. 2DMs can undergo dramatic changes in their fundamental physical properties when they are combined into heterostructures, particularly if their lattices are misaligned: an example is how graphene becomes superconducting when the alignment of two layers is “twisted” by specific angles of a few degrees. This new field of “twistronics” explores how properties of 2D materials can be tailored for specific applications by stacking them together. We will harness unique capabilities of Linear-Scaling DFT to design 2DM heterostructures “ab initio” for future application in electronic devices that combining high performance and ultra-low power usage. There will be opportunities to use Machine Learning tools to accelerate these simulations, and to develop theoretical spectroscopy methods that enable prediction and interpretation of state-of-the-art experimental results. Anas Siddiqui (Cohort 3) Nicholas Hine, Neil Wilson

Accelerating Theoretical Spectroscopy through Machine-learning

Theoretical approaches to predicting and interpreting advanced spectroscopy techniques for investigating energy and charge transfer processes at the nanoscale will be modelled and accelerated with machine-learning tools. Carlo Maino (Cohort 1) Nicholas Hine

Nanoscale material discovery for thermoelectric energy harvesting and cooling

This project aims to exploit electronic and vibrational properties of nanoscale materials that are 1000 times smaller than diameter of a human hair to discover new materials for energy harvesting and cooling in consumer electronics such as mobile phones and laptops. The ultimate goal of the research is to understand quantum and phonon transport through molecular structures for thermoelectricity and thermal management. The candidate will receive a broad training on computational materials modelling and gain experience with cutting edge quantum transport simulation methods, conduct a vibrant research with publication potential and would have an opportunity to conduct collaborative projects with internationally leading experimental groups in Europe and beyond. For more information visit www.nanolab.uk James M. Targett (Cohort 2) Hatef Sadeghi

Simulating Surface Spectroscopy of Single Atom Magnets and Catalysts

Functional materials for catalysis and magnetooptical applications often contain precious materials, the scarcity of which is a strong motivation for maximizing the efficiency of their use. Single atoms can act as catalysts (Single atom catalysts, SACs) or single atom magnets (SAMs) when stabilized on well-defined substrates. SAC/SAM materials are often studied with X-ray photoelectron spectroscopy and X-ray absorption spectroscopy, but the rich structure and overlapping features make these spectra hard to disentangle. This project will develop new simulation methodology to predict such complex spectroscopic signatures and, in collaboration with experimental partners, novel SAM and SAC materials will be characterized. Dylan Morgan (Cohort 3) Reinhard Maurer, Julie Staunton

A Game of Order Parameters

Predicting if, how and when ice crystals will form in clouds (and our own cells!) is important to atmospheric science (and cryopreservation!) and manufacture of pharmaceuticals. Katarina Blow (Cohort 1) David Quigley

Automatic Prediction and Characterisation of Complex Chemical Reactions

Around 90% of all chemical processes use catalysis to control reactivity and selectivity, yet the design of new catalysts too often depends on informed trial-and-error to make progress. Idil Ismail (Cohort 1) Scott Habershon

Exploring the Heterogeneous Biogenesis Pathways of the Bacterial Cell Envelope

The cell envelope serves as the front-line to both defence and pathogenicity in bacteria. Critical protein assemblies are required to mature, localise and assemble proteins, sugars and lipids around the cell to enable protection against antibiotics, phages and toxins, and to modulate cell structure and shape. A major component of this is the essential extracellular cell wall, which forms a mesh-like coat around the cell. Its assembly is the target for many antibiotics. Here we will use molecular simulation to study the protein machinery responsible for the formation of the cell wall and other biogenesis pathways within the cell envelope. Matyas Parrag (Cohort 3) Phillip Stansfield and David Roper

It's all in the Structure: Transforming drug design by bringing together molecular simulations and machine learning

The solubility of pharmaceutical drugs determines to what extent they can be absorbed. Machine learning algorithms can predict the solubility of novel drugs without the need of actually synthetizing them - thus saving substantial time and money. However, we currently infer solubility from the structure of single molecules in vacuum - a sub-optimal approach ignoring interatomic interactions. This project, supported by AstraZeneca, will address this pitfall by generating three-dimensional molecular models of crystalline drugs polymorphs and simulate their dissolution by means of enhanced sampling simulations. These results will be used to construct a machine learning framework that will unravel the atomistic origins of drugs solubility. Steven Tseng (Cohort 2) Gabriele Sosso
Next generation sampling of organic molecules Models of simple flexible organic molecules, such as methane or carbon dioxide, have surprisingly rich phase diagrams showing numerous (and sometimes spurious) stable and metastable crystal structures. These models are of utmost interest in areas from organic electronics to pharmaceuticals. Traditional computer modelling of these systems requires laborious application of multiple techniques. We will instead adapt a novel “one shot” data-intensive algorithm (nested sampling) which makes both structural and thermodynamic predictions, to flexible organic molecules. We will use the resulting tool to automatically calculate full phase diagrams, initially for simple “toy” alkane models and ultimately realistic heterogeneous organic systems. In Progress Omar Adesida (Cohort 2)
Predicting long-term materials ageing using reaction discovery and machine learning Predicting the long-term (decades or more) stability of organic polymeric materials under ambient environmental photothermal conditions is a unique challenge because experimental testing on such time-scales is often impossible or too expensive. In this project, we will merge reaction discovery tools (Habershon group) with machine-learning energy calculation methods (Maurer Group) to develop kinetic models to predict the long-term behaviour of organic polymeric materials. These predictive models will then have potential to be used to guide the choice of materials with tailored properties for long-term environmental applications. In Progress Joseph Gilkes (Cohort 2)
Protein origami: New computational methods to predict protein folding ensembles How does a protein fold into its native state? This is one of the most important and challenging problems in the chemical sciences, and a key question in understanding diseases driven by protein misfolding and aggregation (such as Parkinson’s disease). In this project, we will develop and employ a new computational scheme to access long time-scale protein folding events by mapping onto a discretized connectivity-based description of protein structure. This new approach will then enable us to investigate sequence-specific folding effects and translational folding, as well as providing a new scheme for protein-structure prediction. In Progress Ziad Fakhoury (Cohort 3)
Untangling disorder in amorphous pharmaceuticals using artificial intelligence The best pharmacologically active compound may have been found, but its function, properties and processability are heavily dependent on the solid form. Solid-state Nuclear Magnetic Resonance and diffraction experiments, coupled with quantum mechanical calculations are powerful tools to elucidate the atomic structure, which is needed to understand and control macroscopic properties. The challenge in amorphous systems is that experiments only provide globally averaged measurements, while large length scales are needed to capture the disorder, which render quantum mechanical calculations unfeasible. In this project, machine learning models will be used to bypass the need for these costly computations, allowing interpretation of experimental data and structure determination with unprecedented accuracy. In Progress Jeremy Thorn (Cohort 3)