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Can Machine Learning Potentials Be More Accurate Than the Methods They Are Trained On?
Although machine learning potentials (MLPs) generalize across configurational and chemical compound spaces, their accuracy is commonly believed to be limited by their training methods, with improvements beyond this limit achieved so far only by refitting to experimental observables. In this Perspect
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