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Training Dataset Matters for Machine Learning Interatomic Potentials: Water Simulation as an Example
Accurate predictions of machine learning interatomic potentials (MLIPs) rely on training datasets covering relevant thermodynamic conditions and phases. Using water as an example, we show that insufficiently sampled datasets of MLIPs lead to wrong predictions of densities, melting temperatures, and
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