Machine-learned, finite temperature Fermi-operator expansions suitable for GPUs and AI-hardware
- DOI
- 10.1063/5.0347394
- Published
- 2026-10-14
- Container
- The Journal of Chemical Physics
- Publisher
- AIP Publishing
- Open access
- unknown
Credibility signals
uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- supportingDOI registered: A matching record was returned by Crossref.
- supportingDOI resolves: A matching record was returned by Crossref.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- supportingMetadata completeness: All 6 scored descriptive metadata groups are present.
Cite this work
BibTeX
@article{allodium:10.1063/5.0347394,
title = {Machine-learned, finite temperature Fermi-operator expansions suitable for GPUs and AI-hardware},
author = {Stanislaw Kowalski and Christian F. A. Negre and Anders M. N. Niklasson and Kipton Barros and Joshua Finkelstein},
year = {2026},
journal = {The Journal of Chemical Physics},
doi = {10.1063/5.0347394},
url = {https://doi.org/10.1063/5.0347394}
}RIS
TY - JOUR TI - Machine-learned, finite temperature Fermi-operator expansions suitable for GPUs and AI-hardware AU - Stanislaw Kowalski AU - Christian F. A. Negre AU - Anders M. N. Niklasson AU - Kipton Barros AU - Joshua Finkelstein PY - 2026 JO - The Journal of Chemical Physics DO - 10.1063/5.0347394 UR - https://doi.org/10.1063/5.0347394 ER -
APA
Kowalski, S., Negre, C. F. A., Niklasson, A. M. N., Barros, K., & Finkelstein, J. (2026). Machine-learned, finite temperature Fermi-operator expansions suitable for GPUs and AI-hardware. The Journal of Chemical Physics. https://doi.org/10.1063/5.0347394
Source records
- crossref · retrieved 2026-11-03T12:03:07.767Z