The DOI matches its Crossref record.
Determining atom‐centered monopoles from molecular electrostatic potentials. The need for high sampling density in formamide conformational analysis
Abstract
An improved method for computing potential‐derived charges is described which is based upon the CHELP program available from QCPE.1 This approach (CHELPG) is shown to be considerably less dependent upon molecular orientation than the original CHELP program. In the second part of this work, the CHELPG point selection algorithm was used to analyze the changes in the potential‐derived charges in formamide during rotation about the CN bond. In order to achieve a level of rotational invariance less than 10% of the magnitude of the electronic effects studied, an equally‐spaced array of points 0.3 Å apart was required. Points found to be greater than 2.8 Å from any nucleus were eliminated, along with all points contained within the defined VDW distances from each of the atoms. The results are compared to those obtained by using CHELP. Even when large numbers of points (ca. 3000) were sampled using the CHELP selection routine, the results did not indicate a satisfactory level of rotatational invariance. On the basis of these results, the original CHELP program was found to be inadequate for analyzing internal rotations.
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.
Integrity signals
Registry
Cites
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.
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
Cited by
Formation of chiral morphologies through selective binding of amino acids to calcite surface steps
Orme, C. A., Noy, A., Wierzbicki, A., McBride, M. T., Grantham, M., Teng, H. H., Dove, P. M., & DeYoreo, J. J. (2001) · Nature · doi:10.1038/35081034 · Cited by 700
J -Aggregates of Cyanine Dye for NIR-II in Vivo Dynamic Vascular Imaging beyond 1500 nm
Sun, C., Li, B., Zhao, M., Wang, S., Lei, Z., Lu, L., Zhang, H., Feng, L., Dou, C., Yin, D., et al. (2019) · Journal of the American Chemical Society · doi:10.1021/jacs.9b10043 · Cited by 582
Machine learning molecular dynamics for the simulation of infrared spectra
Gastegger, M., Behler, J., & Marquetand, P. (2017) · Chemical Science · doi:10.1039/c7sc02267k · Cited by 556
Liu, Z., Lu, T., & Chen, Q. (2020) · Carbon · doi:10.1016/j.carbon.2020.04.099 · Cited by 429
Doubly ionic hydrogen bond interactions within the choline chloride–urea deep eutectic solvent
Ashworth, C. R., Matthews, R. P., Welton, T., & Hunt, P. A. (2016) · Physical Chemistry Chemical Physics · doi:10.1039/c6cp02815b · Cited by 404
Chung, Y. G., Gómez-Gualdrón, D. A., Li, P., Leperi, K. T., Deria, P., Zhang, H., Vermeulen, N. A., Stoddart, J. F., You, F., Hupp, J. T., et al. (2016) · Science Advances · doi:10.1126/sciadv.1600909 · Cited by 321
Sorbents for CO2 capture from flue gas—aspects from materials and theoretical chemistry
Hedin, N., Chen, L., & Laaksonen, A. (2010) · Nanoscale · doi:10.1039/c0nr00042f · Cited by 243
Structural basis of second-generation HIV integrase inhibitor action and viral resistance
Cook, N. J., Li, W., Berta, D., Badaoui, M., Ballandras-Colas, A., Nans, A., Kotecha, A., Rosta, E., Engelman, A. N., & Cherepanov, P. (2020) · Science · doi:10.1126/science.aay4919 · Cited by 108
Murugan, N. A., Nordberg, A., & Ågren, H. (2018) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.8b00093 · Cited by 92
Martínez, S., Cruz, V. L., Ramos, J., & Martínez-Salazar, J. (2012) · Organometallics · doi:10.1021/om2007776 · Cited by 33
Dehydrochlorination of Hexachlorocyclohexanes Catalyzed by the LinA Dehydrohalogenase. A QM/MM Study
Manna, R. N., Zinovjev, K., Tuñón, I., & Dybala-Defratyka, A. (2015) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.5b07538 · Cited by 21
From QM/MM to ML/MM: A new era in multiscale modeling
Grassano, J. S., Pickering, I., Roitberg, A. E., Estrin, D. A., & Semelak, J. A. (2025) · Chemical Physics Reviews · doi:10.1063/5.0260078 · Cited by 20
Performance-Based Screening of Porous Materials for Carbon Capture
Farmahini, A. H., Krishnamurthy, S., Friedrich, D., Brandani, S., & Sarkisov, L. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01266 · Cited by 7
Metadata
- Authors
- Breneman, C. M., & Wiberg, K. B.
- Venue
- Journal of Computational Chemistry
- Publisher
- Wiley
- Published
- 1990-04
- Volume
- 11
- Issue
- 3
- Pages
- 361-373
- ISSN
- 0192-8651 (ISSN-L)
- 1096-987X
- OpenAlex
- W2151334055
- Cited by
- 4,790
- References
- 0
- FWCI
- 6.19
- 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
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.