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Electrons, atoms and molecules for catalysis, medicines and devices

Available Projects for Autumn 2022 entry

For further guidance on how to apply, student funding and the HetSys training programme, please visit the Study with Us page.

Project Title



Optimising power grids and chemical reactions with graph neural networks

Albert Bartok-Partay;
Reinhard J. Maurer

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.

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Physics, Chemistry, Engineering, Molecules, Machine Learning, Neural Networks

Biosensing with molecular nanoribbons

Sara Sangtarash;
Rebecca Notman

DNA sequencing (sensing the order of bases in a DNA strand) is an essential step toward personalized medicine for improving human health. Despite recent developments, conventional DNA sequencing methods are still expensive and time consuming. This project aims to exploit theoretically an alternative strategy for quantum sensing of biological species such as DNA using changes in the electrical properties of a membrane (e.g. molecular nanoribbons containing a pore) upon translocation of biospecies. It will also establish design principles to use molecular nanoribbons for a new generation of quantum devices for selective sensing of biospecies.

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Quantum, Devices, Transport, Engineering, Physics, Chemistry, Atomistic

Harnessing Molecular Simulations to advance Electronics and Photovoltaics: design rules for the selective deposition of metals by novel condensation techniques

Gabriele C. Sosso; Ross Hatton

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.

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Atomistic, quantum, chemistry, molecules, electronics, photovoltaics

Applying Machine Learning to understand photoprotection: how do triazine-based UV-filters really work?

Nicholas Hine; Vas Stavros

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 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 a complex solvent environment. One main target of this will be triazine-based UV filters found in commercial sunscreen formulations.

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Quantum, Atomistic, Chemistry, Molecules, Machine Learning

Reliable quantum algorithms for plasma and fusion physics

Animesh Datta; Tom Goffrey

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. The project is ideal for a student interested in a close interplay of quantum computation and simulation with plasma physics.

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Quantum, Plasma, Physics, Mathematics, Continuum

Machine learning based quantum emulators to simulate light-driven catalysis

Reinhard J. Maurer;
James Kermode

Industrial catalysis must become sustainable within our lifetime. This means creating renewable fuels and fertilizer to ensure food safety from clean energy such as sunlight and sustainable feedstocks such as atmospheric CO2 and N2. To achieve this, we need to be able to understand the mechanisms behind photocatalytic processes and how light excitation can selectively break chemical bonds. This is currently limited by the sheer computational cost of quantum mechanical simulation of light-driven chemistry. The aim of this project will be to create and apply machine learning models that emulate the quantum mechanical interaction between light and molecules at surfaces.

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Catalysis, Quantum, Chemistry, Materials, Molecules, Physics, Machine Learning

Artificial Intelligence (AI)-enabled Cryogenic Electron Ptychography For Bio-macromolecule Imaging

Peng Wang

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.

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Physics, Life Sciences, Machine Learning, Imaging

Memory matters : Beyond Markovian models of rare event kinetics

David Quigley

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.

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Molecules, Atomistic, Rare Events, Physics, Chemistry