PhD Studentship in AI-supported Computational Chemistry of Materials

Project title: Atomistic Simulations of Surface Chemistry underpinning the Atomic-Scale Processing of Materials for AI-driven Nanoelectronics Applications

Supervisor: Dr. Bora Karasulu (Assistant Professor in Computational Chemistry, Chemistry Department)

Start Date: As soon as possible, by 1^st September 2024 at latest

Applications are invited for a 3-year funded PhD studentship, sponsored by the Intel-Merck consortium (the AWASES programme), available as soon as possible (at latest by September 2024). The postholder will work with Dr. Bora Karasulu (University of Warwick, Chemistry Department) on the “Atomistic Simulations of Surface Chemistry underpinning the Atomic-Scale Processing of Materials for AI-driven Nanoelectronics Applications” project.

Atomic layer deposition (ALD) and atomic layer etching (ALE) are pivotal in semiconductor processing, particularly with the advancement of smaller and more complex nanoelectronics devices. They offer sustainable processing by employing minimal materials and chemicals through self-limiting reactions, and their reproducibility across different runs and facilities makes them ideal for predictive AI models throughout process development, monitoring, and control. The progression of AI-driven process development relies on comprehensive training data accessible via databases. Supported by two major semiconductor players, Intel and Merck, this research programme aims to expand our crowd-sourced databases containing ALD and ALE processes to enable AI-compatible atomic-level processing approaches. It also explores novel avenues for AI-driven process development, material design, and autonomous experimentation to decrease material consumption and enhance sustainability in semiconductor fabrication.

The programme unites three research groups from the University of Warwick, Eindhoven University of Technology (TU/e, Netherlands), and the L3S Research Centre at Leibniz University of Hannover (L3S, Germany). It focuses on three perspectives: experimental process development, materials modelling, and data science, aiming to integrate experimental and AI-predicted data into computational workflows. As a crucial part of the research programme, our group conducts multi-scale modelling of chemical processes at material surfaces in semiconductors, underpinning ALD and ALE processes. We also engage in high-throughput discovery of coating materials and in-silico characterisation of their properties, facilitating direct comparison with experiments.

The PhD candidate in our group will utilise state-of-the-art computational modelling methods such as Density Functional Theory (DFT) and machine-learning interatomic potentials (MLIPs), contributing to their development and running calculations on high-performance computing (HPC) facilities. Our research is closely coordinated with experimentalists and computer scientists, offering ample opportunities for collaboration and interaction.

More details on the PhD position can be found here. For specifics regarding the formal application process, refer to http://www.go.warwick.ac.uk/pgapply (see research course applications). When applying for this post online (see below), please attach the following documents: (1) Your CV including your most up to date qualifications and a summary of your education to date along with the contact details for two academic referees, and list of publications (if any)and (2) a supporting (personal) statement (max 2 pages) outlining your interest in pursuing a PhD within the research area of the studentship, particularly in the advertised project, and your suitability for the role. Referees will be contacted at the interview stage, which is expected to take place in the week commencing May 6^th.

Please direct informal enquiries and requests for further information to Dr. Bora Karasulu (bora.karasulu@warwick.ac.uk). For further details about the research group, see https://warwick.ac.uk/fac/sci/chemistry/staff/borakarasulu.

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