Scaled opposite-spin atomic-orbital based algebraic diagrammatic construction scheme for the polarization propagator with asymptotic linear-scaling effort: theory and implementation
A novel local approach for the quantum-chemical computation of excited states is presented, where the concept of the atomic-orbital formulation of the second-order Møller-Plesset energy expression is extended to the second-order algebraic diagrammatic construction scheme by virtue of the Laplace tra...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article (Journal) |
| Language: | English |
| Published: |
30 March 2023
|
| In: |
The journal of chemical physics
Year: 2023, Volume: 158, Issue: 12, Pages: 1-16 |
| ISSN: | 1089-7690 |
| DOI: | 10.1063/5.0139894 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1063/5.0139894
|
| Author Notes: | M.A. Ambroise, F. Sacchetta, D. Graf, C. Ochsenfeld, and A. Dreuw |
| Summary: | A novel local approach for the quantum-chemical computation of excited states is presented, where the concept of the atomic-orbital formulation of the second-order Møller-Plesset energy expression is extended to the second-order algebraic diagrammatic construction scheme by virtue of the Laplace transform. The scaled opposite-spin second-order algebraic diagrammatic construction method with Cholesky decomposed densities and density-fitting, or CDD-DF-SOS-ADC(2) for short, exploits the sparsity of the two-electron repulsion integrals, the atomic ground-state density matrix, and the atomic transition density matrix to drastically reduce the computational effort. By using a local density-fitting approximation, it is shown that asymptotically linear scaling can be achieved for linear carboxylic acids. For electron-dense systems, sub-cubic scaling can be achieved if the excitation is local, and hence the transition density is sparse. Furthermore, the memory footprint and accuracy of the CDD-DF-SOS-ADC(2) method are explored in detail. |
|---|---|
| Item Description: | Gesehen am 17.05.2023 |
| Physical Description: | Online Resource |
| ISSN: | 1089-7690 |
| DOI: | 10.1063/5.0139894 |