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Opportunities

Postdoc and PhD openings become available on a rolling basis. Informal inquiries via email are welcome.

Start dates and topics are flexible. Topics are in the general areas of:

  • Development of nonadiabatic and approximate quantum dynamics methods for condensed phase systems
  • Machine learning of electronic structure
  • Simulation of ultrafast dynamics at surfaces and light-driven catalysis

Specific current opportunities:

A fully funded 4-year PhD project in "Deep learning of reaction barriers for high-throughput retrosyntetic drug design" as part of the CDT in Modelling of Heterogeneous Systems [HetSys] (October 2024 start)

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.

For more details about the project and the HetSys CDT, see here: https://warwick.ac.uk/fac/sci/hetsys/themes/projectopportunities/hp2024-08

A fully funded 4-year PhD project in Machine-learning-guided design of efficient sustainable materials for hydrogenation photocatalysis (flexible 2023-2024 start date)

The project is open to candidates with a science Bachelor/Master degree (Chemistry, Physics, Mathematics, Computer Science) and covers 4-year stipends including 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 (r.maurer@warwick.ac.uk) with their CV and a motivation statement. Details on the formal application procedure can be found at https://warwick.ac.uk/study/postgraduate/apply/research/.


A fully funded 4-year PhD project in Computational design of topological defects in graphene (flexible 2023-2024 start date)

A fully funded 4-year PhD project in “Computational design of topological defects in graphene” is available with a flexible 2023-2024 start date. The project is open to candidates with a science Bachelor/Master’s degree (Physics, Chemistry, Materials Science) and includes a 4-year stipend with 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 nanomaterial graphene is ultrathin, ultra-strong and highly conductive. However, to find new real-world applications it must be tailored to a given function, e.g. to be more adhesive or to sense gases. Idealised graphene is a two-dimensional sheet of pure carbon. Its topology is defined by linked hexagonal rings of carbon; each hexagon perfectly identical to the next. By topologically designing defects to build graphene block by block with non-hexagonal rings, non-carbon atoms, or missing carbon atoms, we can controllably introduce new functionality to graphene that will enhance its applicability to support metal catalysts, to sense molecules, and to be used as components in nanoelectronic devices.

The Project: The goal of this PhD project is to develop and employ computational simulation and electronic structure theory approaches to identify how certain local defects can be created in two-dimensional graphene networks. The simulations will be done in close collaboration with experiment, in fact, they will directly inform which experiments will be conducted. We will aim to identify general design principles of local topological defect formation to identify which defects can be preserved in networks and which ones cannot. The project will also involve method and software development to enable large-scale simulations on high performance computing architectures such as the UK national supercomputer. Where surface superstructure models become too large for direct quantum mechanical calculations, acceleration techniques based on machine-learning methods will be employed. Upon successful completion of the computational screening and characterisation of defective graphene networks, we will study the ability of the grown networks to support new types of metal catalyst materials and nanostructured magnetic materials.

The studentship is part of a collaborative project funded by the Leverhulme Trust and will feature close collaboration with experimental project partners who synthesize organic molecular precursors and who perform surface synthesis and characterization of defective graphene. The student will work closely with experimental team members to directly inform which experiments will be conducted and to provide computational simulation support for the characterization of the structure and spectroscopic properties of experimentally fabricated two-dimensional networks.

Successful candidates will join a large, interdisciplinary research group that provides a collaborative and supportive environment. The PhD student will be trained in state-of-the-art electronic structure theory, molecular simulation methods, and machine learning methods, all of which are well established in the host group. The student will acquire important transferable skills such as software development (Python) and project management and present their research at international and national conferences.

Interested candidates should contact Prof. Reinhard Maurer (r.maurer@warwick.ac.uk).


A fully funded 4-year PhD project in property-driven generative machine learning for tailored materials design (flexible 2023-2024 start date)

A fully funded 4-year PhD project in property-driven generative machine learning for tailored materials design is available with a flexible 2023-2024 start date. The project is open to candidates with a science Bachelor/Master degree (Chemistry, Physics, Computer Science) and includes a 4-year stipend with full tuition fees. Successful candidates will become members of the interdisciplinary computational research group led by Prof. Reinhard Maurer (www.warwick.ac.uk/maurergroup) based in the Departments of Physics and Chemistry at the University of Warwick, UK.

When chemists search for new functional molecules with tailored properties, they synthetically modify known structural motifs of molecules by trial and error. This empirical exploration of the space of possible chemical compounds forms the present-day bread-and-butter business of chemical innovation. In the last few decades, quantum chemistry has further aided this process by accurately predicting properties in silico, based on formal quantum theory, if provided with molecular structures and significant computational resources. However, certain applications require molecules to satisfy specific properties, for example the emission of light of a specific colour for novel organic light-emitting diode (OLED) materials. Here it might be secondary what the structure of the molecule is as long as it is easy to synthesise. Trial-and-error based on structural modification is unlikely to identify the best and most efficient material; at best, it would be very time consuming. Machine learning methods are ideal to address this inverse property-driven design task. Generative deep learning models are extremely powerful for image generation (deep fake images) and large language models (e.g. ChatGPT). They can also be used to generate new molecules.

The Project: The goal of this PhD project is to develop generative machine learning models that can create new undiscovered molecules with tailormade electronic and optical properties for specific applications in OLED materials, organic solar cells, and quantum sensing applications. The student will use existing and generate new data to train generative machine learning models and use advanced techniques to condition and bias such models so that the model predicts molecules with specific narrow properties such as light emission properties or ease of synthesizability. The project will also explore how this approach can be extended to more complex materials, such as molecular crystals, surface nanostructures, and metal nanoparticles.

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 and project management. Substantial resources are available for group members to attend international workshops and conferences.

Interested candidates should contact Prof. Reinhard Maurer (r.maurer@warwick.ac.uk).

Group Openings

Applications from students interested in research at the Master's, PhD and PostDoc level are always welcome.

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