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of this WASP-financed project is machine learning, in particular dealing with generative models and instabilities associated with cycles of retraining on mixtures of human and machine-generated data
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. This project will rely on recent advances in neural networks to develop machine learning potentials (MLPs) for MD simulations of realistic nanomaterial/coolant-liquids and use these to gain fundamental insights