Machine learning-accelerated quantum chemical modelling of molecular junctions and surface catalytic reactions

About the Project

We are looking for a highly motivated and talented PhD candidate to join the UKRI Future Leaders Fellowship (FLF) research project of Dr. Zsuzsanna Koczor-Benda on “Quantum embedding for functional nanodevice design” in the Department of Chemistry at the University of Warwick.

Project outline:

Modelling light-driven processes and charge transfer across molecule-metal interfaces is instrumental for the development of next-generation molecular optoelectronic devices for medical imaging and reaction monitoring, as well as for the development of sustainable photocatalysts. In this role you will develop machine learning (ML)-accelerated quantum mechanics in quantum mechanics (QM-in-QM) methods to enable the accurate simulation of charge transfer and light-matter interactions at interfaces. You will model surface catalytic reactions and surface spectroscopy to support the property-driven design of molecules for ultrasensitive imaging and nano-optoelectronics applications.

The project is supported by a range of national and international partners and provides ample opportunities for professional development and networking, including regular research visits to collaborators and travel to international scientific events. You will have opportunities to be involved in collaborative research projects in directions of theoretical method/software development, theoretical-experimental studies, and developing real-world applications. The group has access to significant high-performance computing resources for the project at the Scientific Computing Research Technology Platform at Warwick.

Project outcomes:

You will contribute to the development of widely used quantum chemistry software packages by introducing new functionalities for modelling interfaces via QM-in-QM embedding. You will demonstrate the applicability of the ML-accelerated QM-in-QM methodology on molecule-metal interfaces by simulating surface catalytic reactions and spectroscopy. This project will directly support molecular optoelectronics design and catalyst design research themes within the group, providing opportunities for you to collaborate on such projects. Your work will be published in high-profile journals and disseminated at international conferences, software developer and user meetings.

Skills and training:

You will acquire skills in programming (e.g. Python, FORTRAN, bash), demonstrable by your contributions to the development of quantum chemistry software and stand-alone tools. You will get familiar with a wide range of computational and quantum chemistry modelling techniques, scientific machine learning, optimisation methods, and high-performance computing. You will have the opportunity to contribute to molecular design projects, gaining knowledge in generative AI, database handling and analysis. You will develop collaborative, project management, presentation and writing skills by working with project partners, presenting your research at conferences, developing tutorials and writing research papers. Your professional development will also be supported by a wide range of training opportunities available at the University of Warwick.

About the research group:

We are enthusiastic about developing new computational techniques accelerated by machine learning to simulate quantum chemical properties of molecules at interfaces and harness AI tools to design functional molecules for ultrasensitive and miniaturized sensing and optoelectronics applications. The group has a wide collaborative network covering electronic structure method development, research software engineering, machine learning, nanoelectronics and nanophotonics. We are one of seven research groups at Warwick Computational and Theoretical Chemistry (CaTCh) and we are part of Warwick Quantum and Warwick Centre for Predictive Modelling (WCPM) interdisciplinary research communities spanning several departments, including Chemistry, Physics, Computer Science and Engineering.

Find out more about the research group at https://koczorbenda.wordpress.com/

About you:

You are enthusiastic about working at the boundary of theoretical method development, machine learning, and quantum chemical modelling. You will have a background in chemistry, physics, materials science, or related subject and you are driven by an interest in understanding quantum chemical properties of molecules and harnessing them for real-world applications. You enjoy working in a collaborative research team, where you can contribute your expertise and skills to deliver new software capabilities and groundbreaking applications to fulfil an ambitious research vision.

Requirements and eligibility:

Applicants must have, or be predicted to obtain, a good degree (2.1 or 1st class) in Chemistry, or other relevant scientific discipline (e.g. Physics, Materials Science). Candidates with experience in ab initio electronic structure methods, scientific programming, or scientific machine learning are particularly encouraged. We welcome applications from all suitably qualified candidates, and particularly encourage applications from under-represented groups

How to apply:

Apply via the University’s online application portal. The position will remain open until filled, so don’t hesitate to express your interest well in advance. Starting date is expected to be October 2026, but it is open for discussion.

For enquiries please contact:

Dr Zsuzsanna Koczor-Benda, zsuzsanna.koczor-benda@warwick.ac.uk

Funding Notes

This is a fully funded 4 -year PhD studentship (incl. home fees plus annum stipend) which is subject to funding restrictions and hence open to UK nationals and those of equivalent status (more information on home fee status here).