Top-Down versus Bottom-Up Approaches for σ‑Functionals\nBased on the Approximate Exchange Kernel

Recently, we investigated\na number of so-called σ- and τ-functionals\nbased on the adiabatic-connection fluctuation–dissipation theorem\n(ACFDT); particularly, extensions of the random phase approximation\n(RPA) with inclusion of an exchange kernel in the form of an antisymmetrized\nHartree kernel. One of these functionals, based upon the approximate\nexchange kernel (AXK) of Bates and Furche, leads to a nonlinear contribution\nof the spline function used within σ-functionals, which we previously\navoided through the introduction of a simplified “top-down”\napproach in which the σ-functional modification is inserted\na posteriori following the analytic coupling strength integration\nwithin the framework of the ACFDT and which was shown to provide excellent\nperformance for the GMTKN55 database when using hybrid PBE0 reference\norbitals. In this work, we examine the analytic “bottom-up”\napproach in which the spline function is inserted a priori, i.e.,\nbefore evaluation of the analytic coupling strength integral. The\nnew bottom-up functionals, denoted σ↑AXK, considerably\nimprove upon their top-down counterparts for problems dominated by\nself-interaction and delocalization errors. Despite a small loss of\naccuracy for noncovalent interactions, the σ↑AXK@PBE0\nfunctionals comprehensively outperform regular σ-functionals,\nscaled σ-functionals, and the previously derived σ+SOSEX-\nand τ-functionals in the WTMAD-1 and WTMAD-2 metrics of the\nGMTKN55 database.

Publication Details

Journal
Figshare
Published
2025-01-09
DOI
https://doi.org/10.1021/acs.jpca.4c05289.s001
Primary Topic
Plant Pathogens and Resistance
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Top-Down versus Bottom-Up Approaches for σ‑Functionals\nBased on the Approximate Exchange Kernel

Figshare
Plant Pathogens and Resistance
article

Top-Down versus Bottom-Up Approaches for σ‑Functionals\nBased on the Approximate Exchange Kernel

article en

Abstract

Recently, we investigated\na number of so-called σ- and τ-functionals\nbased on the adiabatic-connection fluctuation–dissipation theorem\n(ACFDT); particularly, extensions of the random phase approximation\n(RPA) with inclusion of an exchange kernel in the form of an antisymmetrized\nHartree kernel. One of these functionals, based upon the approximate\nexchange kernel (AXK) of Bates and Furche, leads to a nonlinear contribution\nof the spline function used within σ-functionals, which we previously\navoided through the introduction of a simplified “top-down”\napproach in which the σ-functional modification is inserted\na posteriori following the analytic coupling strength integration\nwithin the framework of the ACFDT and which was shown to provide excellent\nperformance for the GMTKN55 database when using hybrid PBE0 reference\norbitals. In this work, we examine the analytic “bottom-up”\napproach in which the spline function is inserted a priori, i.e.,\nbefore evaluation of the analytic coupling strength integral. The\nnew bottom-up functionals, denoted σ↑AXK, considerably\nimprove upon their top-down counterparts for problems dominated by\nself-interaction and delocalization errors. Despite a small loss of\naccuracy for noncovalent interactions, the σ↑AXK@PBE0\nfunctionals comprehensively outperform regular σ-functionals,\nscaled σ-functionals, and the previously derived σ+SOSEX-\nand τ-functionals in the WTMAD-1 and WTMAD-2 metrics of the\nGMTKN55 database.

Figshare
Openalex Percentile: Top 42%
Plant Pathogens and Resistance
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.