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also exploit machine-learning techniques to train more approximate simulation methods with highly accurate reference DFT results. This will allow simulation of system sizes that are difficult to treat
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) simulations. The use of MLFFs will allow for larger simulation cells and longer timescales to be accessed, whilst retaining the accuracy of DFT calculations, which we hope will allow for more accurate
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approximate simulation methods with highly accurate reference DFT results. This will allow simulation of system sizes that are difficult to treat with fully ab initio theoretical approaches. Due to the project
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applications such as energy storage, solar, and carbon capture. The project will explore methods beyond traditional density-functional theory (DFT), leveraging cutting-edge techniques such in machine learning