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Title: Reduced scaling CASPT2 using supporting subspaces and tensor hyper-contraction

Journal Article · · Journal of Chemical Physics
DOI:https://doi.org/10.1063/1.5037283· OSTI ID:1468951
 [1]; ORCiD logo [1]
  1. Stanford Univ., Stanford, CA (United States); SLAC National Accelerator Lab., Menlo Park, CA (United States)
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Here, we present a reduced scaling formulation of the state specific complete active space second-order perturbation method (CASPT2) requiring O(N4) operations and O(N2) memory for a fixed active space, where N is proportional to system size. Motivated by the properties of the Kronecker sum, we introduce the supporting subspace technique (SST), which decomposes the CASPT2 linear equations into two parts: a single-reference MP2 energy term using dressed orbitals, plus a reduced linear system with dimension scaling as O(N2). Together with Laplace quadrature, the SST allows us to reformulate CASPT2 using a MP2 energy computation and Fock builds. By further applying the tensor hyper-contraction (THC) approximation, the MP2-like term can be computed with O(N4) operations, and the remainder can be solved with O(N3) operations using the preconditioned conjugate gradient method. This is the first application of THC in the context of multi-reference methods. We also developed an efficient implementation of the method by utilizing graphical processing units and exploiting spatial sparsity in tensor operations. We benchmark the accuracy of the new method against conventional CASPT2 for reactions in the gas phase. We apply the new method to Menshutkin SN2 reactions in carbon nanotubes, demonstrating the feasibility of CASPT2 calculations with O(100) atoms.

Research Organization:
SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC02-76SF00515; ACI-1429830; CHE-1565249
OSTI ID:
1468951
Journal Information:
Journal of Chemical Physics, Vol. 149, Issue 4; ISSN 0021-9606
Publisher:
American Institute of Physics (AIP)Copyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 29 works
Citation information provided by
Web of Science

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Cited By (3)

Making machine learning a useful tool in the accelerated discovery of transition metal complexes journal July 2019
CASSCF linear response calculations for large open-shell molecules journal May 2019
Ab Initio Exact Diagonalization Simulation of the Nagaoka Transition in Quantum Dots text January 2019

Figures / Tables (7)

FIG. 1(p. 4)figure FIG. 1
FIG. 2(p. 9)figure FIG. 2
FIG. 3(p. 11)figure FIG. 3
FIG. 4(p. 13)figure FIG. 4
FIG. 5(p. 15)figure FIG. 5
FIG. 6(p. 16)figure FIG. 6
FIG. 7(p. 17)figure FIG. 7

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