arXiv:2601.19966v2 Announce Type: replace-cross Abstract: We introduce ELECTRAFI, a fast, end-to-end differentiable model for predicting periodic charge densities in crystalline materials. ELECTRAFI constructs anisotropic Gaussians in real space and exploits their closed-form Fourier transforms to analytically evaluate plane-wave coefficients via the Poisson summation formula. This formulation delegates non-local and periodic behavior to analytic transforms, enabling reconstruction of the full periodic charge density with a single inverse FFT. By avoiding explicit real-space grid probing, peri
The increasing demand for accelerated materials discovery and computational efficiency in research drives the development of faster predictive models.
This development significantly speeds up the prediction of material properties, which is crucial for advancing fields like electronics, energy storage, and drug discovery.
Traditional computationally intensive methods for charge density prediction are now being challenged by significantly faster, AI-driven approaches, reducing resource requirements.
- · Materials science researchers
- · Pharmaceutical R&D
- · Computational chemistry software developers
- · AI/ML in scientific computing
- · Developers of slower, less efficient simulation software
- · Research groups reliant on older, compute-heavy methods
Faster material design cycles due to rapid property prediction.
Reduced computational costs for material research allowing more iterative experimentation.
Acceleration of new material breakthroughs in domains like superconductors, batteries, and catalysts.
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