This is a project which is currently making use of HPC facilities at Newcastle University. It is active.
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This project will use the HPC facility to perform large-scale simulations of solid-state battery materials, with a particular focus on understanding ion transport, defect chemistry, and interfacial stability at the atomic scale. First-principles calculations, advanced molecular dynamics, and machine-learned force field approaches will be employed to explore battery material behaviour under realistic operating conditions. The computational insights generated will support the design and optimisation of next-generation high-performance all-solid-state batteries with improved safety, capacity, and cycle life. This research will contribute to accelerating the development of sustainable energy storage technologies.
This project will make use of a combination of established and emerging computational tools to perform large-scale simulations of solid-state battery materials on the HPC facility. Density functional theory calculations will be conducted using VASP to investigate electronic structure, defect formation and interfacial energetics. Classical and machine-learning molecular dynamics simulations will be performed using LAMMPS combined with PLUMED for enhanced sampling techniques. DeePMD-kit will be utilised to develop accurate neural-network-based interatomic potentials capable of modelling large systems over extended time scales. Python will support workflow automation, data analysis, job submission, and visualisation. Together, these tools will enable efficient high-performance computation across multiple scales.