Asymptotic Evaluation of Benchmark Screening Functionals in the Scalar Temporal Field Ontology

We analyze the asymptotic behavior of benchmark nonlocal screening functionals in the Scalar Temporal Field Ontology (STFO). Building on the previously derived semi-analytic formulation, we examine the limits of strong and weak screening corresponding to large and small values of the dimensionless parameter x = μa. In both regimes, the screening functional simplifies, allowing a qualitative determination of the ordering of screening strength across the 1s, 2s, and 2p bound states. We find that both asymptotic limits support the hierarchy 1s > 2s > 2p. The intermediate regime remains the key domain for full quantitative evaluation of the screening functional and provides the primary test of the STFO framework.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-03-22
DOI
https://doi.org/10.5281/zenodo.19169416
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Asymptotic Evaluation of Benchmark Screening Functionals in the Scalar Temporal Field Ontology

Cale Scott Howe
Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Materials Science
article

Asymptotic Evaluation of Benchmark Screening Functionals in the Scalar Temporal Field Ontology

Cale Scott Howe
article en

Abstract

We analyze the asymptotic behavior of benchmark nonlocal screening functionals in the Scalar Temporal Field Ontology (STFO). Building on the previously derived semi-analytic formulation, we examine the limits of strong and weak screening corresponding to large and small values of the dimensionless parameter x = μa. In both regimes, the screening functional simplifies, allowing a qualitative determination of the ordering of screening strength across the 1s, 2s, and 2p bound states. We find that both asymptotic limits support the hierarchy 1s > 2s > 2p. The intermediate regime remains the key domain for full quantitative evaluation of the screening functional and provides the primary test of the STFO framework.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 14%
Machine Learning in Materials Science
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.