Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 985

The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.

Using Hessian Updating To Increase the Efficiency of a Hessian Based Predictor-Corrector Reaction Path Following Method

Hratchian, H. P., Schlegel, H. B.·2005·Journal of Chemical Theory and Computation

Abstract

The reaction path is a key concept in the theoretical description of a chemical reaction. The intrinsic reaction coordinate is defined as the steepest descent path in mass-weighted Cartesian coordinates that connects the transition state to reactants and products on the potential energy surface. Recently, a new Hessian based predictor-corrector reaction path following algorithm was presented that is comparable to a fourth-order algorithm developed earlier. Although the method is very accurate, it is costly because second derivatives of the energy are required at each step. In this work, the efficiency of the method is greatly enhanced by employing Hessian updating. Three different updating schemes have been tested: Murtagh and Sargent, Powell-symmetric Broyden, and Bofill. Bofill's update performs the best and yields excellent speed-up.

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 · 10.1021/ct0499783

Cites

46 of 46 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 46 references

Cited by

See all 985

Metadata

Authors
Hratchian, H. P., & Schlegel, H. B.
Venue
Journal of Chemical Theory and Computation
Published
2004-10-21
Volume
1
Issue
1
Pages
61-69
ISSN
  • 1549-9618 (ISSN-L)
  • 1549-9626
OpenAlex
W1973820731
Cited by
985
References
46
FWCI
0.61
Type
journal article
Language
en
Open access
closed

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: Using Hessian Updating To Increase the Efficiency of a Hessian Based Predictor-Corrector Reaction Path Following Method. https://app.cochat.ai/papers/10.1021/ct0499783
The paper
Hratchian, H. P., & Schlegel, H. B. (2005). Using Hessian Updating To Increase the Efficiency of a Hessian Based Predictor-Corrector Reaction Path Following Method. Journal of Chemical Theory and Computation. https://doi.org/10.1021/ct0499783

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.