PhD in Quantum Materials Physics and Machine Learning

PhD in Quantum Materials Physics and Machine Learning

PhD Project Proposal: Quantum Materials Physics and Machine Learning

University of Birmingham | Supervisor: Prof. Andrew J. Morris

Overview:A competitively funded PhD UK studentship is available focusing on quantum mechanics to discover and understand novel materials for critical applications such as energy storage, solar, and carbon capture. The project will explore methods beyond traditional density-functional theory (DFT), leveraging cutting-edge techniques such as machine learning / artificial intelligence (AI) and/or correlated electron approaches (e.g. DMFT) to address limitations in accuracy and computational feasibility for complex or large-scale systems.

Background and Motivation:Challenges in materials science demand solutions that go beyond both existing materials and methods. While DFT has been the cornerstone of quantum mechanical materials calculations, its limitations hinder progress in studying complex systems, such as materials with strong electronic correlations or those with function over large length- or timescales. Addressing these challenges is key to understanding degradation in battery materials, designing efficient energy storage devices, and predicting the behaviour of emerging materials.

Recent advances in artificial intelligence, particularly the development of machine-learned interatomic potentials, have shown promise in extending the reach of computational structure prediction. These methods, pioneered by Andrew’s group, allow for the efficient exploration of crystalline and amorphous material structures, greatly accelerating the discovery process. We are also interested in using dynamical mean-field theory (DMFT) to study electronic correlations in materials with complex degradation mechanisms, such as advanced battery materials.

What the project looks like day-to-day:Some fractions of:

1. Analytical techniques to develop the underlying algorithms/methods.
2. Coding and scripting in e.g. C(++), Python, Julia, BASH.
3. Utilizing regional and national high-performance computing facilities (both CPU and GPU-based) to conduct large-scale simulations efficiently.
4. Working closely with experimental collaborators to validate computational predictions, ensuring relevance to real-world applications.

The project scope is quite flexible and can be tailored to the successful applicant's interests.

Candidate Profile:This project is ideal for candidates with a strong background in physics, materials science, or chemistry, and an interest in computational methods. Prior experience with quantum mechanics, ML, or high-performance computing is advantageous but not essential.

Application Process:Interested candidates are encouraged to contact Andrew at to discuss the project further and receive guidance on preparing a strong application.

Seniority level

• Internship

Employment type

• Full-time

Job function

• Research, Analyst, and Information Technology
• Industries: Higher Education

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