mlsanityData to 7 Oct 2026

A Recipe for Charge Density Prediction

arXiv 2405.19276 ↗First posted 29 May 2024physics.comp-ph cs.LGcited by 8

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Abstract (arXiv)

In density functional theory, charge density is the core attribute of atomic systems from which all chemical properties can be derived. Machine learning methods are promising in significantly accelerating charge density prediction, yet existing approaches either lack accuracy or scalability. We propose a recipe that can achieve both. In particular, we identify three key ingredients: (1) representing the charge density with atomic and virtual orbitals (spherical fields centered at atom/virtual coordinates); (2) using expressive and learnable orbital basis sets (basis function for the spherical fields); and (3) using high-capacity equivariant neural network architecture. Our method achieves state-of-the-art accuracy while being more than an order of magnitude faster than existing methods. Furthermore, our method enables flexible efficiency-accuracy trade-offs by adjusting the model/basis sizes.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 29 of its 69 references that are in the map. First posted on arXiv 29 May 2024.

Builds on

29 of 69 references.

References · 29

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17 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 5.

8 papers.

  1. 2025ELECTRA: A Cartesian Network for 3D Charge Density Prediction with Floating Orbitalscited by 9
  2. 2025Towards A Transferable Acceleration Method for Density Functional Theorycited by 2
  3. 2026Global Plane Waves From Local Gaussians: Periodic Charge Densities in a Blinkcited by 1
  4. 2026ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Densitycited by 1
  5. 2024E3STO: Orbital Inspired SE(3)-Equivariant Molecular Representation for Electron Density Predictioncited by 0
  6. 2026Multitask learning with semiempirical orbital charges enables sample-efficient MLIPscited by 0
  7. 2026Neural-Network Solutions to Real-Space Charge Density and Generalizationcited by 0
  8. 2026Complete Neural Electronic Initialization Accelerates Materials DFTcited by 0