Deep learning density functionals for gradient descent optimization

Abstract

Machine-learned regression models represent a promising tool to implement accurate and computationally affordable energy-density functionals to solve quantum many-body problems via density functional theory. However, while they can easily be trained to accurately map ground-state density profiles to the corresponding energies, their functional derivatives often turn out to be too noisy, leading to instabilities in self-consistent iterations and in gradient-based searches of the ground-state density profile. We investigate how these instabilities occur when standard deep neural networks are adopted as regression models, and we show how to avoid it using an ad-hoc convolutional architecture featuring an inter-channel averaging layer. The testbed we consider is a realistic model for noninteracting atoms in optical speckle disorder. With the inter-channel average, accurate and systematically improvable ground-state energies and density profiles are obtained via gradient-descent optimization, without instabilities nor violations of the variational principle.

9 pages, 10 figures

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• Machine learning and density functional theory
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Cited by in corpus (5)

• Supervised learning of random quantum circuits via scalable neural networks
• Deep learning nonlocal and scalable energy functionals for quantum Ising models
• Leveraging Normalizing Flows for Orbital-Free Density Functional Theory
• Solving deep-learning density functional theory via variational autoencoders
• Challenges and opportunities in the supervised learning of quantum circuit outputs