Can machine learning uncover viable non-platinum catalysts for hydrogen evolution? An unsupervised clustering approach to transition metal–ligand complexes

Achouak Benarbia, Ali Alshami, Ayyaz Mustafa, Olusegun Stanley Tomomewo

Open source

DOI: 10.1039/d6ra03405e
Published: 2026
Container: RSC Advances
Publisher: Royal Society of Chemistry (RSC)
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. Source: Crossref; license: CC0 metadata
supportingDOI resolves: A matching record was returned by Crossref. Source: Crossref; license: CC0 metadata
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. Source: DOAJ; license: CC0
not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredROR affiliation: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
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. Source: Retraction Watch; license: CC BY 4.0
not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
not scoredOpen access status: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredPublication license: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
supportingMetadata completeness: All 6 scored descriptive metadata groups are present. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer

Cite this work

BibTeX

@article{allodium:10.1039/d6ra03405e,
  title = {Can machine learning uncover viable non-platinum catalysts for hydrogen evolution? An unsupervised clustering approach to transition metal–ligand complexes},
  author = {Achouak Benarbia and Ali Alshami and Ayyaz Mustafa and Olusegun Stanley Tomomewo},
  year = {2026},
  journal = {RSC Advances},
  doi = {10.1039/d6ra03405e},
  url = {https://doi.org/10.1039/d6ra03405e}
}

RIS

TY  - JOUR
TI  - Can machine learning uncover viable non-platinum catalysts for hydrogen evolution? An unsupervised clustering approach to transition metal–ligand complexes
AU  - Achouak Benarbia
AU  - Ali Alshami
AU  - Ayyaz Mustafa
AU  - Olusegun Stanley Tomomewo
PY  - 2026
JO  - RSC Advances
DO  - 10.1039/d6ra03405e
UR  - https://doi.org/10.1039/d6ra03405e
ER  -

APA

Benarbia, A., Alshami, A., Mustafa, A., & Tomomewo, O. S. (2026). Can machine learning uncover viable non-platinum catalysts for hydrogen evolution? An unsupervised clustering approach to transition metal–ligand complexes. RSC Advances. https://doi.org/10.1039/d6ra03405e

Source records

crossref · retrieved 2026-09-26T16:59:23.544Z