Quantum
Electrons, atoms and molecules for catalysis, medicines and devices
Projects in Progress
| Project Title |
Description |
Research Student | Supervisor(s) |
|---|---|---|---|
| Atomistic insight into nucleation and electrochemistry: Machine learning multiscale simulation |
Developing battery technologies requires atomistic insight into electrochemistry, nucleation, and degradation, but simulation is presented with a challenging combination of lengthscale, timescale and accuracy demands. This presents a great opportunity for Scientific Machine Learning to work closely with experimental techniques such as transmission electron microscopy, and to learn to simulate nucleation and electrochemistry processes. In this project, we will use machine learned interatomic potentials to make simulated training data for ML models of nucleation. This will be paired with TEM imaging that captures atomic-level electrochemical processes in situ on 2D materials as they occur and constrains and informs our models. |
Luca Seaford (Cohort 6) |
Nick Hine |
| Charting a course towards new light-activated molecules | Extreme Space Weather is driven by large-scale eruptions from the Sun called coronal mass ejections. Upon arrival at the Earth, these produce amazing auroral displays but also endanger satellites and astronauts, and disrupt communications and power grids. Forecasting these events is of primary importance to the UK MET Office, one of three centres world-wide providing round-the-clock space weather predictions. This project, in collaboration with the UK MET Office, will develop state-of-the-art plasma simulations to forecast extreme Space Weather and develop advanced statistical techniques to quantify the uncertainties in their prediction. |
Scott Habershon, Nick Hine | |
| Complex Electronic Structures for Thermoelectric Energy Materials | Two thirds of all energy used is lost into heat during conversion processes, which puts enormous pressure on energy sustainability. Thermoelectric materials convert waste heat into electricity and can provide solutions towards this problem. Recently, a myriad of materials and compounds with complex electronic structures have been synthesized, offering possibilities for exceptional thermoelectric performance. The project uses Density Functional Theory coupled to advanced electronic transport methods, to examine the potential of the most prominent materials, targeting appropriate electronic structure designs for further optimization. The richness of experimental data, both from literature and in house, will aid towards theory validation. |
Neophytos Neophytou, Gavin Bell |
|
| Machine-learning quantum surrogate models to simulate energy transport across interfaces | Modern technologies such as photocatalysis or laser nanolithography involve energy transfer across interfaces. Many critical societal challenges require that we transfer light or electronic energy more efficiently into chemical energy, e.g., to utilize CO2 as renewable fuel. To achieve this, we need to understand the mechanisms behind the intricate dynamics that unfold at interfaces. Quantum mechanical simulations provide electronic-structure insights but are computationally intractable for relevant systems. The aim of this project is to create and apply machine learning models that emulate the quantum mechanical interaction of light, electrons, and atoms for many thousands of atoms at realistic interfaces. |
Reinhard Maurer, James Kermode | |
| Reliable quantum simulations of plasma and fusion physics | The field of quantum computation and simulations seeks to develop efficient quantum algorithms for problems that are classically inefficient to solve and are therefore computationally expensive. Furthermore, a quantum-enhanced simulation must not only perform a hard classical simulation efficiently, but also correctly. The latter goal is particularly important as real-world quantum computers are noisy and error prone. This project will develop efficient quantum simulations for problems in plasma and fusion physics, and establish their reliability in real-world quantum computers. |
Animesh Datta, Tom Goffrey | |
| What's that made of? Modelling muonic X-ray radiation for quantitative elemental analysis | Muonic X-ray elemental analysis is a non-destructive technique that can be used to study the elemental composition of a whole sample: from micrometres down to centimetres below its surface. Some of the current applications of this technique are to determine the composition of ancient archaeological samples, which cannot sustain any type of physical damage, and also to study meteorites, biological samples and functional materials. This project aims to develop a robust method to model the muonic X-ray spectra quantitatively. This can be achieved by numerically solving the Dirac equation, that describes all the muon transitions occurring in the experiment. The theoretical and methodological developments will be implemented in MuDirac, which is a modern, open-source, sustainable software tool that is being used to aid in muonic X-ray elemental analysis. |
Albert Bartok-Partay, Nick Hine | |
| Machine Learning Multiscale Simulation Of Photoconductivity In Correlated Oxides | Predicting, explaining and modelling novel behaviours of quantum materials requires a combination of theoretical insight with state-of-the-art multiscale modelling. In the case of complex oxides, displaying both strong electronic correlation and a diverse range of extended and point defects, traditional electronic structure methods encounter severe challenges when trying to model key properties such as photoconductivity and bulk photovoltaic effects. Fortunately, the extraordinary speed and power of machine-learned interatomic potentials provides a brand-new way to gain insight into these systems. This project will design and build multiscale models to understand photoconductivity in SrTiO3, particularly enhancement associated with dislocation cores. |
Nick Hine, Reinhard Maurer | |
| Memory matters : Beyond Markovian models of rare event kineticsLink opens in a new window | Rare events involve rapid but infrequent transitions between two states of a system, e.g. a metastable parent liquid A and a stable crystal B. This project will extend the state-of-the-art for models of how often a system transitions from state A to state B with two key developments; (1) techniques from signal processing to fit models of collective dynamics which incorporate memory (2) machine learning of committor functions, the probability that a microstate will evolve to B before returning to A. Combined, these two developments will allow us to make enhanced predictions from atomistic simulations. | Hubert Naguszewski (Cohort 4) | David Quigley |
| Artificial Intelligence (AI)-enabled Cryogenic Electron Ptychography For Bio-macromolecule ImagingLink opens in a new window | Ptychography is an emerging computational microscopy technique for acquiring images with resolutions beyond the limits imposed by lenses. It has been applied to high-resolution X-ray imaging in synchrotrons and accurate wavefront-sensing in space telescopes. Instead of macroscopic objects, our work aims to visualise the basic building blocks of life (e.g. proteins) in 3D towards a near-atomic resolution by developing ptychography in conjunction with Nobel-prize winning cryogenic electron microscopy (cryo-EM). Our analysis will be enhanced by artificial intelligence and machine techniques developed in this project. We will work closely with experimentalists at the medical research centre of the Rosalind Franklin Institute, and structural biologists at the University of Oxford. | Yu Lei (Cohort 4) | Peng Wang |
| Optimising power grids and chemical reactions with graph neural networksLink opens in a new windowLink opens in a new window | In this project the wider applicability of graph neural networks will be explored, to answer questions as "How do atoms arrange in space to form molecules and materials?" and "How does power flow in an electrical grid?". The common theme is that both problems may be represented as graphs: atoms or substations as the vertices, and bonds or transmission lines as the edges. GNNs will be employed to model interactions in such systems and to optimise processes. Results of this work will be useful in optimising electricity grid operations and schedules as well as in understanding chemical transitions between different molecules. | Mariia Radova (Cohort 4) | Albert Bartok-Partay; Reinhard J. Maurer |
| Harnessing Molecular Simulations to advance Electronics and Photovoltaics: design rules for the selective deposition of metals by novel condensation techniquesLink opens in a new window | Copper and silver are key to electronics and photovoltaics. However, depositing in a controlled manner these metals on a given surface is a slow and costly process. We have recently discovered that a thin layer of specific organofluorine compounds enables the selective deposition of copper and silver. This unconventional approach is fast and inexpensive, but our understanding of its molecular-level details is presently very limited. This project will investigate the interplay between the metal-organofluorine interaction strength and the polymer-polymer intermolecular interactions by combining density functional theory and classical molecular dynamics - thus identifying concrete design rules to further electronics and photovoltaics. | Arielle Fitkin (Cohort 4) | Gabriele C. Sosso; Ross Hatton |
| Applying Machine Learning to understand photoprotection: how do triazine-based UV-filters really work? Link opens in a new window |
Ultraviolet radiation (UVR) has far-reaching consequences on life such as skin cancer in humans and damage to photosynthetic machinery in plants. This project will study the fundamental mechanisms that provide naturally-occurring molecules with photoprotective properties, allowing them to absorb UVR and dissipate it harmlessly as heat. We will harness the power of Machine-Learned Potential Energy Surfaces to accelerate accurate-but-slow electronic structure calculations based on Time- Dependent Density Functional Theory. This will enable calculations of properties such as excited state lifetimes in a complex solvent environment. One main target of this will be triazine-based UV filters found in commercial sunscreen formulations. | Jacob Eller (Cohort 4) | Nicholas Hine; Vas Stavros |
| 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 |
| 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 |
| 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. | Omar Adesida (Cohort 2) | Livia Bartok-Partay, David Quigley |
| 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. | Joseph Gilkes (Cohort 2) | Scott Habershon, Reinhard Maurer |
| 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. | Ziad Fakhoury (Cohort 3) | Scott Habershon, Gabriele Sosso |
| 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. | Jeremy Thorn (Cohort 3) | Albert Bartok-Partay, Steven Brown |