A symmetry-preserving and transferable representation for learning the Kohn-Sham density matrix

Liwei Zhang; Patrizia Mazzeo; Michele Nottoli; Edoardo Cignoni; Lorenzo Cupellini; Benjamin Stamm

doi:10.1039/d5dd00230c

January 1, 2025Digital DiscoveryOpen Access

A symmetry-preserving and transferable representation for learning the Kohn-Sham density matrix

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Key Points

The aim is to develop a representation that preserves symmetry and is transferable for learning the Kohn-Sham density matrix.
Developed a new representation approach for the Kohn-Sham density matrix.
Utilized density functional theory (DFT) principles in the modeling.
Assured symmetry preservation in the representation.
Demonstrated significant improvements in representing Kohn-Sham density matrix properties.
Revealed that the new representation can be applied across different physical scenarios.

Implication

This research demonstrates a novel approach to understand the Kohn-Sham density matrix, suggesting improved modeling in density functional theory.

Abstract

The Kohn-Sham (KS) density matrix is one of the most essential properties in KS density functional theory (DFT), from which many other physical properties of interest can be derived. In...

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Cite This Study

Zhang et al. (2025) studied this question.

synapsesocial.com/papers/69c8c35cde0f0f753b39e1db https://doi.org/10.1039/d5dd00230c

View Full Paper

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A symmetry-preserving and transferable representation for learning the Kohn-Sham density matrix

Key Points

Implication

Abstract

Discussion

Cite This Study

A symmetry-preserving and transferable representation for learning the Kohn-Sham density matrix

Key Points

Implication

Abstract

Discussion

Cite This Study

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