The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Accelerating Density Functional Calculations with Graphics Processing Unit
Abstract
An algorithm is presented for graphics processing units (GPUs), which execute single-precision arithmetic much faster than commodity microprocessors (CPUs), to calculate the exchange-correlation term in ab initio density functional calculations. The algorithm was implemented and applied to two molecules, taxol and valinomycin. The errors in the total energies were about 10(-5) a.u., which is accurate enough for practical usage. If the exchange-correlation term is split into a simple analytic model potential and the correction to it, and only the latter is calculated with the GPU, the energy error is decreased by an order of magnitude. The resulting time to compute the exchange-correlation term is smaller than it is on the latest CPU by a factor of 10, indicating that a GPU running the proposed algorithm accelerates the density functional calculation considerably.
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
Cites
Molecular Electronic‐Structure Theory
Helgaker, T., Jørgensen, P., & Olsen, J. (2000) · doi:10.1002/9781119019572 · Cited by 2,594
Cited by
Ufimtsev, I. S., & Martinez, T. J. (2009) · Journal of Chemical Theory and Computation · doi:10.1021/ct9003004 · Cited by 916
Deep learning for computational chemistry
Goh, G. B., Hodas, N. O., & Vishnu, A. (2017) · Journal of Computational Chemistry · doi:10.1002/jcc.24764 · Cited by 809
Quantum Chemistry on Graphical Processing Units. 2. Direct Self-Consistent-Field Implementation
Ufimtsev, I. S., & Martinez, T. J. (2009) · Journal of Chemical Theory and Computation · doi:10.1021/ct800526s · Cited by 451
Metadata
- Authors
- Yasuda, K.
- Venue
- Journal of Chemical Theory and Computation
- Publisher
- American Chemical Society (ACS)
- Published
- 2008-07-04
- Volume
- 4
- Issue
- 8
- Pages
- 1230-1236
- ISSN
- 1549-9618 (ISSN-L)
- 1549-9626
- OpenAlex
- W2069215288
- Cited by
- 158
- References
- 1
- FWCI
- 8.70
- 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: Accelerating Density Functional Calculations with Graphics Processing Unit. https://app.cochat.ai/papers/10.1021/ct8001046
- The paper
- Yasuda, K. (2008). Accelerating Density Functional Calculations with Graphics Processing Unit. Journal of Chemical Theory and Computation. https://doi.org/10.1021/ct8001046
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.