Over this past decade, we combined the idea of stochastic resolution of identity with a variety of electronic structure methods. In our stochastic Kohn-Sham density functional theory (DFT) method, the density is an average over multiple stochastic samples, with stochastic errors that decrease as the inverse square root of the number of sampling orbitals. Here, we develop a stochastic embedding density functional theory method (se-DFT) that selectively reduces the stochastic error (specifically on the forces) for a selected subsystem(s). The motivation, similar to that of other quantum embedding methods, is that for many systems of practical interest, the properties are often determined by only a small subsystem. In stochastic embedding DFT, two sets of orbitals are used: a deterministic one associated with the embedded subspace and the rest, which is described by a stochastic set. The method agrees exactly with deterministic calculations in the limit of a large number of stochastic samples. We apply se-DFT to study a p-nitroaniline molecule in water, where the statistical errors in the forces on the system (the p-nitroaniline molecule) are reduced by an order of magnitude compared with nonembedding stochastic DFT.
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7 November 2019
Research Article|
November 07 2019
Stochastic embedding DFT: Theory and application to p-nitroaniline in water
Wenfei Li;
Wenfei Li
a)
1
Department of Chemistry and Biochemistry, University of California
, Los Angeles, California 90095, USA
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Ming Chen;
Ming Chen
b)
2
Department of Chemistry, University of California and Materials Sciences Division, Lawrence Berkeley National Laboratory
, Berkeley, California 94720, USA
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Eran Rabani
;
Eran Rabani
c)
2
Department of Chemistry, University of California and Materials Sciences Division, Lawrence Berkeley National Laboratory
, Berkeley, California 94720, USA
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Roi Baer
;
Roi Baer
d)
3
Fritz Haber Center for Molecular Dynamics, Institute of Chemistry, The Hebrew University of Jerusalem
, Jerusalem 91904, Israel
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Daniel Neuhauser
Daniel Neuhauser
e)
1
Department of Chemistry and Biochemistry, University of California
, Los Angeles, California 90095, USA
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a)
Electronic mail: [email protected]
b)
Electronic mail: [email protected]
c)
Electronic mail: [email protected]
d)
Electronic mail: [email protected]
e)
Electronic mail: [email protected]
J. Chem. Phys. 151, 174115 (2019)
Article history
Received:
May 15 2019
Accepted:
October 14 2019
Citation
Wenfei Li, Ming Chen, Eran Rabani, Roi Baer, Daniel Neuhauser; Stochastic embedding DFT: Theory and application to p-nitroaniline in water. J. Chem. Phys. 7 November 2019; 151 (17): 174115. https://doi.org/10.1063/1.5110226
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