Development of Machine-Learned Interatomic Potentials to Predict Structure, Transport, and Reactivity in Platinum-Based Fuel Cells.
- DOI
- 10.1021/acsomega.6c01745
- Published
- 2026 Jun 23
- Container
- ACS omega
- Publisher
- Not recorded
- Open access
- yes
Credibility signals
limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- 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.
- supportingOpen access status: Normalized open-access status: open.
- 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.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Cite this work
BibTeX
@article{allodium:10.1021/acsomega.6c01745,
title = {Development of Machine-Learned Interatomic Potentials to Predict Structure, Transport, and Reactivity in Platinum-Based Fuel Cells.},
author = {Fazel K and Brown S and Clary J and Bose P and Karimitari N and Frischknecht AL and Sundararaman R and Vigil-Fowler D},
year = {2026},
journal = {ACS omega},
doi = {10.1021/acsomega.6c01745},
url = {https://doi.org/10.1021/acsomega.6c01745}
}RIS
TY - JOUR TI - Development of Machine-Learned Interatomic Potentials to Predict Structure, Transport, and Reactivity in Platinum-Based Fuel Cells. AU - Fazel K AU - Brown S AU - Clary J AU - Bose P AU - Karimitari N AU - Frischknecht AL AU - Sundararaman R AU - Vigil-Fowler D PY - 2026 JO - ACS omega DO - 10.1021/acsomega.6c01745 UR - https://doi.org/10.1021/acsomega.6c01745 ER -
APA
K, F., S, B., J, C., P, B., N, K., AL, F., R, S., & D, V. (2026). Development of Machine-Learned Interatomic Potentials to Predict Structure, Transport, and Reactivity in Platinum-Based Fuel Cells.. ACS omega. https://doi.org/10.1021/acsomega.6c01745
Source records
- pubmed · retrieved 2026-09-26T23:35:01.987Z