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The exponential computational cost of describing strongly correlatedelectrons can be mitigated by adopting a reduced density-matrix (RDM)-baseddescription of the electronic structure. While variational two-electron RDM(v2RDM) methods can enable large-scale calculations on such systems, thequality of the solution is limited by the fact that only a subset of knownnecessary N-representability constraints can be applied to the 2RDM inpractical calculations. Here, we demonstrate that violations of partialthree-particle (T1 and T2) N-representability conditions, which can beevaluated with knowledge of only the 2RDM, can serve as physics-based featuresin a machine-learning (ML) protocol for improving energies from v2RDMcalculations that consider only two-particle (PQG) conditions. Proof-ofprinciple calculations demonstrate that the model yields substantially improvedenergies, relative to reference values from configuration-interaction-basedcalculations.

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