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Quantum: Available Projects

Electrons, atoms and molecules for catalysis, medicines and devices

Available Projects for a September 2024 start

For guidance on how to apply, student funding, the integrated HetSys training programme and what life is like in the HetSys CDT, please visit the Study with Us page.

The application window for overseas students has now closed for 24/25 entry.

For details of all our available projects click here.

Title Description
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.

Deep learning of reaction barriers for high-throughput retrosynthetic drug design

The drug discovery pipeline involves the screening of many molecules before viable leads are identified. This involves screening for their pharmacological properties, but also for their synthetic viability. Typical drug molecules can contain up to 100 non-hydrogen atoms, which makes the development of cost-effective and efficient synthetic pathways very challenging. Therefore, high-throughput screening of drug-like molecules needs to also consider their synthetic viability. The aim of this project is to develop a deep learning and generative design toolchain to accurately predict chemical reaction barriers that will advance chemical retrosynthetic design workflows.

Fast simulation algorithms for correlated dynamics in superconducting films and epidemic models  More details to follow 

Learning Collective Dynamics from Accelerated Quantum Jump Monte Carlo

Future quantum technologies preparation and manipulation of quantum systems, but current setups can achieve only limited control. Improving on this requires modelling these experiments, but number of configurations and long timescales[1]inhibits exact numerical approaches. We will circumvent the former by exploiting symmetries in collective open quantum dynamics[2,3]for simulations of hybrid systems in quantum optics[4], and by modifying rare events techniques[5], we will avoid the latter. We will further use dimensional reduction[6]to identify and model[7]phase transitions in experiments. We aim to propose and verify new experimental settings that could support such phenomena.

Noise-to-Signal: Reimagining ultrafast optical spectroscopies to capture fluctuating dynamics in quantum materials

Optical pump-probe spectroscopy is the go-to method for capturing ultrafast (femtosecond) dynamics in solid-state materials and has been used to understand everything from magnetism to photosynthesis. The problem is that pump-probe methods can only capture the ‘average’ dynamics in a system. This means that rapid fluctuations in electronic excitations, critical to phenomena like superconductivity, ion transport or (quantum) phase transitions, remain inaccessible.

This project will revolutionise the information limits of pump-probe spectroscopy. We will build quantitative analytical/computational models of the experiment to derive new, universal, measurement and analysis protocols for capturing ultrafast nonequilibrium physics from pump-probe. We will then apply these methods to explore otherwise ‘hidden’ properties,e.g., quantum entanglement, in solid-state materials. The project is ideal for a student with interests in quantum dynamics, information theory/AI and stochastic physics, with close links to experimentalists.

Are you interesting in applying for this project? Head over to our Study with Us page for information on the application process, funding, and the HetSys training programme

At the University of Warwick, we strongly value equity, diversity and inclusion, and HetSys will provide a healthy working environment, dedicated to outstanding scientific guidance, mentorship and personal development.

HetSys is proud to be a part of the Engineering Department which holds an Athena SWAN Silver award, a national initiative to promote gender equality for all staff and students.