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Interpolative Separable Density Fitting for Accelerating Two-Electron Integrals: A Theoretical Perspective.

Xinming QinWei HuJinglong Yang
Published in: Journal of chemical theory and computation (2023)
Low-rank approximations have long been considered an efficient way to accelerate electronic structure calculations associated with the evaluation of electron repulsion integrals (ERIs). As an accurate and efficient algorithm for compressing the ERI tensor, the interpolative separable density fitting (ISDF) decomposition has recently attracted great attention in this context. In this perspective, we introduce the ISDF decomposition from the theoretical aspects and technique details. The ISDF decomposition can construct a fully separable low-rank approximation (tensor hypercontraction factorization) of ERIs in real space with a cubic cost, offering great flexibility for accelerating high-scaling electronic structure calculations. We review the typical applications of ISDF in hybrid functionals, time-dependent density functional theory, and GW approximation. Finally, we discuss the promising directions for future development of ISDF.
Keyphrases
  • density functional theory
  • molecular dynamics
  • machine learning
  • working memory
  • high resolution
  • deep learning
  • solar cells
  • electron microscopy