Generative AI Models for Materials Discovery

About the Project

The discovery of novel inorganic solid-state materials is essential to advance energy storage, catalysis, semiconductors, and quantum technologies. The design and discovery of these materials is though extremely challenging, making improved methodology of considerable importance. This project aims to develop generative machine learning models to improve crystal structure prediction workflows for identifying new inorganic solids with high accuracy. Generated structures will be validated against physics-based computational methods and benchmarked against existing materials databases.

You will explore cutting-edge techniques in generative modelling (e.g., diffusion models and large language models) and integrate them with chemically-informed constraints and first-principles calculations. The goal is to contribute to AI-driven improvements of the crystal prediction workflow to generate experimental targets, predict their stability and properties, and ultimately accelerate materials discovery beyond current paradigms.

You will join a multidisciplinary research group working at the interface of solid state materials science and AI. You will have access to high-performance computing resources, work closely with experimentalists, and have the opportunity to publish in leading journals. This studentship is suited for a student with a background in computational materials science, machine learning or artificial intelligence. Experience with Python and writing code is essential. Experience with ML frameworks (PyTorch/TensorFlow), graph and/or neural nets and familiarity with materials science, crystallography and/or solid-state chemistry would be an asset. Please clearly highlight your relevant experience in your application.

Candidates wishing to apply should complete the University of Liverpool application form to apply for a PhD in Chemistry

Please review our guide on How to apply for a PhD | Postgraduate research | University of Liverpool carefully and complete the online postgraduate research application form to apply for this PhD project.

Please ensure you include the project title and reference number CCPR176 when applying.

Funding Notes

The UKRI funded Studentship will cover full tuition fees of £5,006 pa. and pay a maintenance grant for 3.5 years, starting at the UKRI minimum of £20,780 pa. for academic year 2025-2026 The Studentship also comes with a Research Training Support Grant to fund consumables, conference attendance, etc.

UKRI Studentships are available to any prospective student wishing to apply including both home and international students. While UKRI funding will not cover international fees, a limited number of scholarships to meet the fee difference will be available to support outstanding international students.