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Constructing "Full-Frequency" Spectra via Moment Constraints for Coupled Cluster Green's Functions
Oliver J Backhouse1, George H Booth1
1Department of Physics, King's College London, Strand, London WC2R 2LS, U.K.
We developed a new method to calculate quasiparticle spectra efficiently using static expectation values. This approach provides a full-frequency spectrum with less computational cost than traditional methods.
Area of Science:
- Quantum chemistry
- Condensed matter physics
- Computational materials science
Background:
- Calculating quasiparticle spectra is crucial for understanding material properties.
- Traditional methods like Green's function approaches can be computationally expensive.
- Accurate spectral representations are needed across all energy scales.
Purpose of the Study:
- To develop an efficient and systematically improvable method for computing full-frequency quasiparticle spectra.
- To leverage static expectation values for spectral moment constraints.
- To reduce the computational cost compared to existing Green's function techniques.
Main Methods:
- Utilizing conserved static expectation values to define spectral distribution moments.
- Employing an expansion based on these moments for spectral reconstruction.
- Computing moment constraints at the coupled-cluster (CC) level.
- Testing the approach on the GW100 benchmark set for charged excitation spectra.
Main Results:
- Demonstrated convergence of correlated state-specific and full spectral quantities.
- Achieved a fraction of the computational effort compared to traditional Green's function methods.
- Successfully converged frontier excitations to the accuracy of the coupled-cluster singles and doubles (CCSD) approximation.
- Obtained a simultaneous representation of the entire excitation spectrum.
Conclusions:
- The proposed method offers an efficient pathway to full-frequency quasiparticle spectra.
- It provides accurate results for frontier excitations and the complete spectrum.
- This approach presents a significant advancement in computational spectroscopy.
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