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Seven useful questions in density functional theory

https://doi.org/10.1007/s11005-023-01665-z

Crisostomo, Steven ; Pederson, Ryan ; Kozlowski, John ; Kalita, Bhupalee ; Cancio, Antonio C. ; Datchev, Kiril ; Wasserman, Adam ; Song, Suhwan ; Burke, Kieron ( April 2023 , Letters in Mathematical Physics)

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Machine learning and density functional theory

https://doi.org/10.1038/s42254-022-00470-2

Pederson, Ryan ; Kalita, Bhupalee ; Burke, Kieron ( June 2022 , Nature Reviews Physics)

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How Well Does Kohn–Sham Regularizer Work for Weakly Correlated Systems?

https://doi.org/10.1021/acs.jpclett.2c00371

Kalita, Bhupalee ; Pederson, Ryan ; Chen, Jielun ; Li, Li ; Burke, Kieron ( March 2022 , The Journal of Physical Chemistry Letters)

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Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics

https://doi.org/doi. 10.1103/PhysRevLett.126.036401

Li, Li ; Hoyer, Stephan ; Pederson, Ryan ; Sun, Ruoxi ; Cubuk, Ekin D. ; Riley, Patrick ; and Burke, Kieron ( January 2021 , Physical review letters)

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 H2 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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