4.8 Article

Kohn-Sham Equations as Regularizer: Building Prior Knowledge into Machine-Learned Physics

期刊

PHYSICAL REVIEW LETTERS
卷 126, 期 3, 页码 -

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AMER PHYSICAL SOC
DOI: 10.1103/PhysRevLett.126.036401

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资金

  1. National Science Foundation [CHE-1856165]
  2. Department of Energy [DE-SC0008696]
  3. U.S. Department of Energy (DOE) [DE-SC0008696] Funding Source: U.S. Department of Energy (DOE)

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Incorporating prior knowledge into machine learning models for physics is crucial for improving generalization performance. Solving the Kohn-Sham equations when training neural networks for the exchange-correlation functional provides implicit regularization, enhancing predictive capabilities effectively.
Including prior knowledge is important for effective machine learning models in physics and is usually achieved by explicitly adding loss terms or constraints on model architectures. Prior knowledge embedded in the physics computation itself rarely draws attention. We show that solving the Kohn-Sham equations when training neural networks for the exchange-correlation functional provides an implicit regularization that greatly improves generalization. Two separations suffice for learning the entire one-dimensional H-2 dissociation curve within chemical accuracy, including the strongly correlated region. Our models also generalize to unseen types of molecules and overcome self-interaction error.

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