  This work studies machine learning for electron density prediction, which is
fundamental for understanding chemical systems and density functional theory
(DFT) simulations. To this end, we introduce the Gaussian plane-wave neural
operator (GPWNO), which operates in the infinite-dimensional functional space
using the plane-wave and Gaussian-type orbital bases, widely recognized in the
context of DFT. In particular, both high- and low-frequency components of the
density can be effectively represented due to the complementary nature of the
two bases. Extensive experiments on QM9, MD, and material project datasets
demonstrate GPWNO's superior performance over ten baselines.
