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Reduced Scaling Formulation of CASPT2 Analytical Gradients Using the Supporting Subspace Method

preprint
submitted on 27.10.2020, 20:54 and posted on 29.10.2020, 09:33 by Chenchen Song, Jeffrey B. Neaton, Todd J. Martínez
We present a reduced scaling and exact reformulation of state specific CASPT2 analytical gradients in terms of the MP2 and Fock derivatives using the supporting subspace method. This work follows naturally from the supporting subspace formulation of the CASPT2 energy in terms of the MP2 energy using dressed orbitals and Fock builds. For a given active space configuration, the terms corresponding to the MP2-gradient can be evaluated with O(N5) operations, while the rest of the calculations can be computed with O(N3) operations using Fock builds, Fock gradients, and linear algebra. When tensor-hyper-contraction is applied simultaneously, the computational cost can be further reduced to O(N4) for a fixed active space size. The new formulation enables efficient implementation of CASPT2 analytical gradients by leveraging existing GPU-based MP2 and Fock routines. We present benchmark results that demonstrate the accuracy and performance of the new method. Example applications of the new method in ab initio molecular dynamics simulation and constrained geometry optimization are given.

Funding

UC-DOE PRIME CONTRACT SECTIONS FOR LBNL (NO. DE-AC02-05CH11231)

SCIDAC PROGRAM UNDER AWARD NUMBER FWP#100385 AND FWP#100384

History

Email Address of Submitting Author

ccsong@lbl.gov

Institution

UC Berkeley, Lawrence Berkeley National Lab

Country

United States

ORCID For Submitting Author

0000-0001-6294-2253

Declaration of Conflict of Interest

None

Version Notes

original submission

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