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We investigate the coupling of different quantum-embedding approaches with a third molecular-mechanics layer, which can be either polarizable or non-polarizable. In particular, such a coupling is discussed for the multilevel...
We present efficient implementations
of the multilevel CC2 (MLCC2)
and multilevel CCSD (MLCCSD) models. As the system size increases,
MLCC2 and MLCCSD exhibit the scaling of the lower-level coupled cluster
model. To treat large systems, we combine MLCC2 and MLCCSD with a
reduced-space approach in which the multilevel coupled cluster calculation
is performed in a significantly truncated molecular orbital basis.
The truncation scheme is based on the selection of an active region
of the molecular system and the subsequent construction of localized
Hartree–Fock orbitals. These orbitals are used in the multilevel
coupled cluster calculation. The electron repulsion integrals are
Cholesky decomposed using a screening protocol that guarantees accuracy
in the truncated molecular orbital basis and reduces computational
cost. The Cholesky factors are constructed directly in the truncated
basis, ensuring low storage requirements. Systems for which Hartree–Fock
is too expensive can be treated by using a multilevel Hartree–Fock
reference. With the reduced-space approach, we can handle systems
with more than a thousand atoms. This is demonstrated for paranitroaniline
in aqueous solution.
We introduce a new
algorithm for the construction of the two-electron
contributions to the Fock matrix in multilevel Hartree–Fock
(MLHF) theory. In MLHF, the density of an active molecular region
is optimized, while the density of an inactive region is fixed. The
MLHF equations are solved in a reduced molecular orbital (MO) basis
localized to the active region. The locality of the MOs can be exploited
to reduce the computational cost of the Fock matrix: the cost related
to the inactive density becomes linear scaling, while the iterative
cost related to the active density is independent of the system size.
We demonstrate the performance of this new algorithm on a variety
of systems, including amino acid chains, water clusters, and solvated
systems.
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