Global Energy Control Condition
The global energy control condition is a set of theoretical inequalities that constrain the behavior of the energy function over the entire configuration space to ensure the sampling reliability of machine learning potential models. It goes beyond local fitting and emphasizes the correctness of the global distribution.
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The study establishes global energy control conditions that can guarantee equilibrium accuracy and identifies the additional conditions required for fast relaxation.
Global energy control conditions typically require the learned potential energy function to satisfy a uniform lower bound or Lipschitz continuity, so that the density ratio of the Boltzmann distribution is bounded over the entire space; the additional conditions for fast relaxation are typically related to the energy barriers and curvature of the potential energy surface, for example requiring that the learned model has no artificial deep local minima.
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The study establishes global energy control conditions that can guarantee equilibrium accuracy and identifies the additional conditions required for fast relaxation.
50%UnverifiedGlobal energy control conditions typically require the learned potential energy function to satisfy a uniform lower bound or Lipschitz continuity, so that the density ratio of the Boltzmann distribution is bounded over the entire space; the additional conditions for fast relaxation are typically related to the energy barriers and curvature of the potential energy surface, for example requiring that the learned model has no artificial deep local minima.
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