Noise-reduced stochastic resolution of identity to CC2 for large-scale calculations via tensor hypercontraction

Abstract: The stochastic resolution of identity (sRI) approximation significantly reduces the computational scaling of CC2 from O(N^5) to O(N^3), where N is a measure of system size. However, the inherent stochastic noise, while controllable, can introduce substantial errors in energy derivatives, limiting its reliability for molecular dynamics simulations. To mitigate this limitation, we introduce a noise-reduced approach, termed THC-sRI-CC2, which synergistically combines the sRI framework with tensor hypercontraction (THC). In this formulation, the expensive Coulomb term, which scales as O(N^4), is decoupled via THC, while the time-determining exchange term with an O(N^5) cost is addressed through the sRI scheme, collectively yielding an overall O(N^3) scaling. Benchmarks demonstrate that our THC-sRI-CC2 implementation achieves greater accuracy and markedly reduced stochastic noise compared to conventional sRI-CC2 with identical computational samplings. The resulting O(N^3) scaling substantially extends the applicability of CC2 for excited-state energy calculations and nonadiabatic dynamics simulations of large molecular systems. Furthermore, this work establishes a general THC-sRI hybrid strategy for the development of reduced-scaling electronic structure methods.