PhD in Chemistry: Generative AI Models for Materials Discovery (PhD)

For PhD Chemistry: Generative AI Models for Materials Discovery, the application deadline for the Sep 2026 intake is Aug 31, 2026, and tuition fees are 28,664 USD per year.

Overview

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.

About this opportunity

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.

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