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, this is 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: Efficient and selective activation of CO2 is a prime target of modern catalysis research and a necessity to transition to a sustainable and decarbonized chemical industry. In this project, you will study the explicit dynamics of light-driven CO2 activation on metal catalysts, i.e. the formation of a CO2 transition state complex from which CO(ads) and O(ads) are formed, using machine-learning-accelerated quantum dynamics simulations. You will contribute to the development of new machine learning methodology to accelerate electronic structure calculations of excited-state phenomena and light-matter interaction in materials.
Interested candidates should contact Prof. Reinhard Maurer ([Email Address Removed]).
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