Data-Driven Refinement of Electronic Energies from Two-Electron Reduced-Density-Matrix Theory.

Autor:	Jones GM; Department of Chemistry, University of Tennessee, Knoxville, Tennessee 37996, United States., Li RR; Department of Chemistry and Biochemistry, Florida State University, Tallahassee, Florida 32306-4390, United States., DePrince AE 3rd; Department of Chemistry and Biochemistry, Florida State University, Tallahassee, Florida 32306-4390, United States., Vogiatzis KD; Department of Chemistry, University of Tennessee, Knoxville, Tennessee 37996, United States.
Jazyk:	angličtina
Zdroj:	The journal of physical chemistry letters [J Phys Chem Lett] 2023 Jul 20; Vol. 14 (28), pp. 6377-6385. Date of Electronic Publication: 2023 Jul 07.
DOI:	10.1021/acs.jpclett.3c01382
Abstrakt:	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.
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu