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Machine learning potentials (MLP) are methods that use first-principles calculation data as training labels and fit interatomic potential energy surfaces and forces with machine learning models, with representative work including Behler-Parrinello symmetry functions, Gaussian approximation potentials (GAP) and neural network potentials (such as ANI and MACE).
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10/18/2026
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