Data-driven Refinement of Electronic Energies from Two-Electron Reduced-Density-Matrix Theory
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866908747947835392 |
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| author | Jones, Grier M. Li, Run. R. DePrince III, A. Eugene Vogiatzis, Konstantinos D. |
| author_facet | Jones, Grier M. Li, Run. R. DePrince III, A. Eugene Vogiatzis, Konstantinos D. |
| contents | The exponential computational cost of describing strongly correlated electrons can be mitigated by adopting a reduced density-matrix (RDM)-based description of the electronic structure. While variational two-electron RDM (v2RDM) methods can enable large-scale calculations on such systems, the quality of the solution is limited by the fact that only a subset of known necessary N-representability constraints can be applied to the 2RDM in practical calculations. Here, we demonstrate that violations of partial three-particle (T1 and T2) N-representability conditions, which can be evaluated with knowledge of only the 2RDM, can serve as physics-based features in a machine-learning (ML) protocol for improving energies from v2RDM calculations that consider only two-particle (PQG) conditions. Proof-of principle calculations demonstrate that the model yields substantially improved energies, relative to reference values from configuration-interaction-based calculations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_12061 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Data-driven Refinement of Electronic Energies from Two-Electron Reduced-Density-Matrix Theory Jones, Grier M. Li, Run. R. DePrince III, A. Eugene Vogiatzis, Konstantinos D. Chemical Physics The exponential computational cost of describing strongly correlated electrons can be mitigated by adopting a reduced density-matrix (RDM)-based description of the electronic structure. While variational two-electron RDM (v2RDM) methods can enable large-scale calculations on such systems, the quality of the solution is limited by the fact that only a subset of known necessary N-representability constraints can be applied to the 2RDM in practical calculations. Here, we demonstrate that violations of partial three-particle (T1 and T2) N-representability conditions, which can be evaluated with knowledge of only the 2RDM, can serve as physics-based features in a machine-learning (ML) protocol for improving energies from v2RDM calculations that consider only two-particle (PQG) conditions. Proof-of principle calculations demonstrate that the model yields substantially improved energies, relative to reference values from configuration-interaction-based calculations. |
| title | Data-driven Refinement of Electronic Energies from Two-Electron Reduced-Density-Matrix Theory |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2305.12061 |