Evidence map›Paper›PMID 27293378›Full record

ArticleTheoretical chemistry accounts2012

Density-functional expansion methods: Grand challenges.

Timothy J Giese, Darrin M York

Abstract read
In one paragraph

Article in Theoretical chemistry accounts, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

  1. DeePMD-GNN: A DeePMD-kit Plugin for External Graph Neural Network Potentials.Journal of chemical information and modeling · 2025
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  4. Doubly Polarized QM/MM with Machine Learning Chaperone Polarizability.Journal of chemical theory and computation · 2021
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  8. Quantum mechanical force fields for condensed phase molecular simulations.Journal of physics. Condensed matter : an Institute of Physics journal · 2017
    Article
  9. Review
  10. Article
  11. Article
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  16. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Timothy J GieseDepartment of Chemistry and Chemical Biology and BioMaPS Institute for Quantitative Biology, Rutgers University, Piscataway, NJ 08854-8087.
Darrin M YorkDepartment of Chemistry and Chemical Biology and BioMaPS Institute for Quantitative Biology, Rutgers University, Piscataway, NJ 08854-8087.

Funding

New Theoretical Tools for BiocatalysisR01GM084149 · NIGMS · UNIVERSITY OF MINNESOTA · PI YORK, DARRIN M · 2008 to 2011
$1.2M
NIGMS NIH HHS R01 GM084149
6 · The paper itself

Abstract

We discuss the source of errors in semiempirical density functional expansion (VE) methods. In particular, we show that VE methods are capable of well-reproducing their standard Kohn-Sham density functional method counterparts, but suffer from large errors upon using one or more of these approximations: the limited size of the atomic orbital basis, the Slater monopole auxiliary basis description of the response density, and the one- and two-body treatment of the core-Hamiltonian matrix elements. In the process of discussing these approximations and highlighting their symptoms, we introduce a new model that supplements the second-order density-functional tight-binding model with a self-consistent charge-dependent chemical potential equalization correction; we review our recently reported method for generalizing the auxiliary basis description of the atomic orbital response density; and we decompose the first-order potential into a summation of additive atomic components and many-body corrections, and from this examination, we provide new insights and preliminary results that motivate and inspire new approximate treatments of the core-Hamiltonian.

Indexed as

density functional theoryelectronic structureTight-binding models

Identifiers

PMID27293378
PMCPMC4898065

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.