Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 237

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

BAGEL: Brilliantly Advanced General Electronic‐structure Library

Open access
Shiozaki, Toru·2018·WIREs Computational Molecular Science

Abstract

On behalf of the development team, I review the capabilities of the Brilliantly Advanced General Electronic‐structure Library (BAGEL) program package in this article. BAGEL is a newly developed full‐fledged program package for electronic‐structure computation in quantum chemistry, which is released under the GNU General Public License with many contributions from the developers. The unique features include analytical CASPT2 nuclear energy gradients and derivative couplings, relativistic multireference wave functions based on the Dirac equation, and implementations of novel electronic structure theories. All of the programs are efficiently parallelized using both threads and MPI processes. We also discuss the code generator SMITH3, which has been used to implement some of the programs in BAGEL. The developers’ contributions are listed at the end of the main text. WIREs Comput Mol Sci 2018, 8:e1331. doi: 10.1002/wcms.1331 This article is categorized under: Electronic Structure Theory > Ab Initio Electronic Structure Methods

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1002/wcms.1331

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 1 references

Cited by

See all 237

Metadata

Authors
Shiozaki, T.
Venue
WIREs Computational Molecular Science
Publisher
Wiley
Published
2017-08-29
Volume
8
Issue
1
ISSN
  • 1759-0876 (ISSN-L)
  • 1759-0884
OpenAlex
W2735018448
Cited by
237
References
1
FWCI
7.82
Type
journal article
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: BAGEL: Brilliantly Advanced General Electronic‐structure Library. https://app.cochat.ai/papers/10.1002/wcms.1331
The paper
Shiozaki, T. (2018). BAGEL: Brilliantly Advanced General Electronic‐structure Library. WIREs Computational Molecular Science. https://doi.org/10.1002/wcms.1331

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.