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April 10, 2026The Journal of Chemical Physics0 citations

Formal O ( N 3) scaling GW calculations by block tensor decomposition for large molecule systems

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YZYueyang ZhangWWWenpei WuPSPeifeng Su

Key Points

  • The aim is to develop an efficient GW algorithm with O(N3) scaling using block tensor decomposition for large molecule systems.
  • Extended the block tensor decomposition algorithm to GW calculations
  • Integrated imaginary-time GW formalism
  • Implemented a real space screening strategy for polarizability
  • Optimized parameters using the JADE algorithm
  • Tested on systems with over 3000 basis functions
  • Achieved scaling of approximately O(N2) in test systems
  • Effective for large molecular systems
  • Established a new approach for eigenvalue-self-consistent GW calculations

Abstract

Within the framework of many-body perturbation theory based on Green's functions, the GW approximation has emerged as a pivotal method for computing quasiparticle energies and excitation spectra. However, its high computational cost and steep scaling present significant challenges for applications to large molecular systems. In this work, we extend the block tensor decomposition (BTD) algorithm, recently developed in our previous work Zhang et al., J. Chem. Phys. 163, 174109 (2025) for low-rank tensor compression, to enable a formally O(N3)-scaling GW algorithm. By integrating BTD with an imaginary-time GW formalism and introducing a real space screening strategy for the polarizability, we achieve an observed scaling of approximately O(N2) in test systems. Key parameters of the algorithm are optimized on the S66 dataset using the JADE algorithm, ensuring a balanced compromise between accuracy and efficiency. Our BTD-based random phase approximation also exhibits O(N2) scaling, and eigenvalue-self-consistent GW calculations become feasible for systems with over 3000 basis functions. This work establishes BTD as an efficient and scalable approach for large-scale GW calculations in molecular systems.

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Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/69d896166c1944d70ce07606https://doi.org/10.1063/5.0319147
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