Grassmann Extrapolationfor Accelerating GeometryOptimization
This study extends the Grassmann extrapolation (G-Ext) method, which was introduced for Born–Oppenheimer molecular dynamics, to the context of geometry optimization. Using density matrices from previous optimization steps, the G-Ext approach applies a nonlinear, structure-preserving mapping onto the Grassmann manifold to provide an initial guess which accelerates the convergence of the self-consistent field (SCF) procedure. Using the optimal parameters identified by employing various descriptors and computational strategies across a diverse set of molecules, G-Ext shows excellent performance improvements, particularly with large molecular systems.
Publication Details
- Journal
- Figshare
- Published
- 2025-02-07
- DOI
- https://doi.org/10.1021/acs.jctc.4c01417.s001
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00