Towards Accelerated SCF Workflows with Equivariant Density-Matrix Learning and Analytic Refinement
We present \textsc{dm-PhiSNet}, a physically constrained \textsc{PhiSNet}-based equivariant model that predicts one-electron reduced density matrices (1-RDMs) directly from molecular geometries in an atomic-orbital (AO) basis for accelerated self-consistent field (SCF) workflows. Training follows a two-stage schedule with progressively introduced physically motivated objectives, and the resulting predictions are refined by a lightweight analytic block. This block enforces electron-number conservation, drives the 1-RDM toward generalized idempotency in the AO metric, and regularizes the occupation spectrum of the Löwdin-orthogonalized density. Across six closed-shell systems -- H$_2$O, CH$...
| Authors | Zuriel Y. Yescas-Ramos, Andrés Álvarez-García, Huziel E. Sauceda |
|---|---|
| Primary category | physics.chem-ph |
| Published | 29 April 2026 |
| Last updated at source | 29 April 2026 |
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