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the noise associated with near-term quantum devices. This in turn offers an exciting new dataset from which it will be possible to use machine learning to train a more accurate functional for use in density
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the noise associated with near-term quantum devices. This in turn offers an exciting new dataset from which it will be possible to use machine learning to train a more accurate functional for use in density
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of materials science, combining ab initio density-functional theory (DFT) calculations with novel ML methods. You will develop ML-assisted computational screening methods, building highly accurate models
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enzyme active site density functional theory (DFT) ‘cluster’ or QM/MM models. The Hough group at Diamond develops methodologies for ambient temperature, time resolved macromolecular crystallography and
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methods to accelerate the discovery of better catalysts that use less platinum and have improved long-term stability. By combining large-scale density functional theory with machine-learned interatomic
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computational project where you will use a variety of methodologies to produce hierarchal 3D lattice structures, e.g. first principles calculations (density functional perturbation theory), molecular dynamics