A review commentary arguing that machine-learned interatomic potentials can carry quantum-level accuracy from small simulations to million-atom studies of carbon phase transitions and nucleation.
Carbon under extreme conditions: Phase boundaries and electronic properties from first-principles theory
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The transformative capability of quantum-accurate machine learning interatomic potentials
A review commentary arguing that machine-learned interatomic potentials can carry quantum-level accuracy from small simulations to million-atom studies of carbon phase transitions and nucleation.