|Funding for:||UK Students, EU Students, International Students|
|Funding amount:||A fully funded 4-year PhD project|
|Placed On:||24th July 2023|
|Closes:||1st March 2024|
Supervisor: Prof. Reinhard J. Maurer, Department of Chemistry & Department of Physics, University of Warwick
Funding: Home, EU, Overseas
Deadline: 01.03.2024, applications will be considered on a rolling basis, please post for 90 days on FindAPhD or until the deadline (on jobs.ac.uk)
A fully funded 4-year PhD project in Machine-learning-guided design of efficient sustainable materials for hydrogenation photocatalysis is available with a flexible 2023-2024 start date. The project is open to candidates with a science Bachelor/Master degree (Chemistry, Physics, Mathematics, Computer Science) and includes a 4-year stipend with full tuition fees. Successful candidates will become members of the interdisciplinary Computational Surface Science group (www.warwick.ac.uk/maurergroup) led by Prof. Reinhard Maurer based in the Departments of Physics and Chemistry at the University of Warwick, UK.
The Scientific Mission: Fuel cells, photovoltaic devices, photocatalytic converters – they all are crucial elements in delivering decarbonization and sustainable energy production at a global scale within the coming decades. They all fundamentally involve energy transfer and chemical dynamics at interfaces where molecules, electrons, and light interact to deliver a certain function. The underlying mechanisms of ultrafast dynamics at surfaces triggered by light or electrons are not well understood, which, for example, limits our ability to design photocatalyst materials that deliver optimal light absorption, catalytic activity, and energy transport. Molecular simulation methods and quantum theoretical calculations in principle can address this but have hitherto struggled with tackling such challenging systems. With the emergence of machine learning methods in the physical sciences, things are rapidly changing. This project is part of a large initiative that aims to tackle this ambitious challenge by developing and applying new software tools that combine machine learning methodology, electronic structure theory, and molecular dynamics methodology to simulate ultrafast chemical dynamics at surfaces and in materials.
Training: Successful candidates will join a large, interdisciplinary research group that provides a collaborative and supportive environment. Projects will often involve teamwork and joint problem solving between colleagues with complementary skills. The successful candidate will be trained in state-of-the-art machine learning methodology, electronic structure theory, and molecular simulation methods. The student will acquire important transferable skills such as software development (Python, Julia) and project management. Substantial resources are available for group members to attend international workshops and conferences. The project is designed to balance method development and application simulation efforts – the latter will include close collaboration with experimental project partners.
The Project: In this project, you will use newly developed machine learning surrogate models to simulate the light-driven promotion of CO hydrogenation to CHO on plasmonic catalyst materials, an important bottleneck reaction in syngas and CO2 reforming. The PhD project will establish the mechanistic details of hot electron interaction with CO and the key design parameters for optimal photocatalytic CO hydrogenation. You will contribute to the development of broadly applicable electronic structure methods and machine learning methods with the specific goal to accelerate the screening of optimal photocatalyst materials.
Interested candidates should contact Prof. Reinhard Maurer (email@example.com).
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