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Generalizability of density functionals learned from differentiable programming on weakly correlated spin-polarized systems
Bhupalee Kalita · Ryan Pederson · Li Li · kieron burke

Mon Dec 13 01:45 PM -- 02:00 PM (PST) @
Event URL: https://openreview.net/forum?id=kXPXTuOtvgU »

Kohn-Sham regularizer (KSR) is a machine learning approach that optimizes a physics-informed exchange-correlation functional within a differentiable Kohn-Sham density functional theory framework. We evaluate the generalizability of KSR by training on atomic systems and testing on molecules at equilibrium. We propose a spin-polarized version of KSR with local, semilocal, and nonlocal approximations for the exchange-correlation functional. The generalization error from our semilocal approximation is comparable to other differentiable approaches. Our nonlocal functional outperforms any existing machine learning functionals by predicting the ground-state energies of the test systems with a mean absolute error of 2.7 milli-Hartrees.

Author Information

Bhupalee Kalita (University of California, Irvine)
Ryan Pederson (University of California, Irvine)
Li Li (Google)
kieron burke (UC Irvine)

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