Machine learning band gaps from the electron density

Abstract

A remarkable consequence of the Hohenberg-Kohn theorem of density functional theory is the existence of an injective map between the electronic density and any observable of the many electron problem in an external potential. In this work, we study the problem of predicting a particular observable, the band gap of semiconductors and band insulators, from the knowledge of the local electronic density. Using state-of-the-art machine learning techniques, we predict the experimental band gaps from computationally inexpensive density functional theory calculations. We propose a modified Behler-Parrinello (BP) architecture that greatly improves the model capacity while maintaining the symmetry properties of the BP architecture. Using this scheme, we obtain band gaps at a level of accuracy comparable to those obtained with state of the art and computationally intensive hybrid functionals, thus significantly reducing the computational cost of the task.

References in corpus (12)

• By-passing the Kohn-Sham equations with machine learning
• Understanding Band Gaps of Solids in Generalized Kohn-Sham Theory
• Probing excitonic states in ultraclean suspended two-dimensional semiconductors by photocurrent spectroscopy
• Prediction model of band-gap for AX binary compounds by combination of density functional theory calculations and machine learning techniques
• Rotationally-invariant mapping of scalar and orientational metrics of neuronal microstructure with diffusion MRI
• Deep Learning and Density Functional Theory
• Band gap and band alignment prediction of nitride based semiconductors using machine learning
• Band gap prediction for large organic crystal structures with machine learning
• Deep learning the Hohenberg-Kohn maps of Density Functional Theory
• Machine learning density functional theory for the Hubbard model
• Learning from the Density to Correct Total Energy and Forces in First Principle Simulations
• Exact Maps in Density Functional Theory for Lattice Models

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