This is a project which is currently making use of HPC facilities at Newcastle University. It is active.
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This ongoing project aims to advance the development of foundational machine-learning interatomic potentials (MLIPs) tailored for complex condensed-phase systems. Our focus includes the accurate modeling of molecular liquids, the structure and dynamics of liquid–solid interfaces relevant to next-generation battery materials, and chemically rich environments central to medicinal and pharmaceutical applications. By integrating high-quality quantum-mechanical data with modern ML architectures, the project seeks to deliver transferable, data-efficient potentials capable of capturing reactive events, long-range interactions, and multicomponent systems with near–first-principles accuracy.