In the problem of approximating physical simulations using density functional theory with neural networks, physical constraints can be imposed on ML models by treating the Kohn-Sham equation as a differentiable model. This greatly improves the accuracy of exchange-correlation term calculations.
TL;DR
In the problem of approximating physical simulations using density functional theory with neural networks, physical constraints can be imposed on ML models by treating the Kohn-Sham equation as a differentiable model. This greatly improves the accuracy of exchange-correlation term calculations.
Why it matters:
Paper URL
https://arxiv.org/abs/2009.08551
Submission Dates(yyyy/mm/dd)
2020/09/17
Authors and institutions
Li Li, Stephan Hoyer, Ryan Pederson, Ruoxi Sun, Ekin D. Cubuk, Patrick Riley, Kieron Burke
Methods
Results
Comments