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
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article

Grassmann Extrapolationfor Accelerating GeometryOptimization

Figshare
Machine Learning in Materials Science
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Grassmann Extrapolationfor Accelerating GeometryOptimization

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Abstract

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.

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Openalex Percentile: Top 61%
Machine Learning in Materials Science
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