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Gaussian Plane-Wave Neural Operator for Electron Density Estimation
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Gaussian Plane-Wave Neural Operator for Electron Density Estimation
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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.
Forward citations
Cited by 2 Pith papers
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ELECTRAFI predicts periodic electron densities by analytically Fourier-transforming a Gaussian mixture, reaching near-SOTA accuracy with up to 633× faster inference and ~20% end-to-end DFT speedups.
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