Charge Density Prediction through 3D-CNN for Fast Convergence of Self-Consistent DFT calculation

The electronic charge density plays an important role in understanding the physical properties of quantum materials. Although the charge density can be obtained by solving the Kohn-Sham equation of density functional theory (DFT), one needs to solve a self-consistent equation which takes time until convergence. Recent studies have tried to directly predict the charge density using machine-learning methods, but the target materials are limited to slabs or organic molecules because they only considered local electronic features [1,2,3].
In this study, we propose a machine-learning algorithm to predict the charge densities of crystals using a three-dimensional convolutional neural network (3DCNN). To deal with the periodicity of crystals, we use FFT grid-based representation and a periodic convolution filter. We demonstrate that our model can predict the charge densities of ABO₃-type crystalline compounds without solving the self-consistent equation. It will accelerate the high-throughput DFT calculations for materials discovery.
[1] F. Brockherde et al. Nat. Commun. 8, 872 (2017)
[2] A. Chandrasekaran et al. npj Comput. Mater. 4, 25 (2018)
[3] Anton V. Sinitskiy and Vijay S. Pande. arXiv:1809.02723, 2018.