Computational Materials Chemistry
Our research vision is to understand, and ultimately to control, materials structure on the atomic scale. We combine quantum mechanics with machine learning (ML) to study relationships of structure, bonding, and properties. Our work is theoretical and computational, but is done in close collaboration with experimental partners, and with practical applications in mind.
Machine-Learning Approaches to Materials Modelling
Computer simulations based on the laws of quantum mechanics are a cornerstone of materials research – but they are severely limited by their computational cost. We develop and apply interatomic potential models that "learn" from quantum-mechanical data, enabling accurate simulations that are many orders of magnitude faster. We are especially interested in building optimised and efficient databases for ML potential fitting, and in ML tools for chemical discovery.
Structures and Properties of Amorphous Inorganic Solids
Amorphous (non-crystalline) materials are a frontier of current materials research: their disordered structures are difficult to determine experimentally, and they also pose large challenges for simulations. We use computer simulations to explore the structures of amorphous functional materials, and aim to link this structural information to physical and chemical properties.