RedoxDB and RedEx: closing the DFT-experiment domain gap in redox potential prediction with transfer learning.

Doan DM, Song Q, Kucernak A, Jelfs KE

DOI: 10.1039/d6sc05970h
Published: 2026 Oct 15
Container: Chemical science
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. Source: Crossref; license: CC0 metadata
cautionDOI resolves: No matching Crossref record was present in this response. 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
supportingOpen access status: Normalized open-access status: open. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
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
cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer

Cite this work

BibTeX

@article{allodium:10.1039/d6sc05970h,
  title = {RedoxDB and RedEx: closing the DFT-experiment domain gap in redox potential prediction with transfer learning.},
  author = {Doan DM and Song Q and Kucernak A and Jelfs KE},
  year = {2026},
  journal = {Chemical science},
  doi = {10.1039/d6sc05970h},
  url = {https://doi.org/10.1039/d6sc05970h}
}

RIS

TY  - JOUR
TI  - RedoxDB and RedEx: closing the DFT-experiment domain gap in redox potential prediction with transfer learning.
AU  - Doan DM
AU  - Song Q
AU  - Kucernak A
AU  - Jelfs KE
PY  - 2026
JO  - Chemical science
DO  - 10.1039/d6sc05970h
UR  - https://doi.org/10.1039/d6sc05970h
ER  -

APA

DM, D., Q, S., A, K., & KE, J. (2026). RedoxDB and RedEx: closing the DFT-experiment domain gap in redox potential prediction with transfer learning.. Chemical science. https://doi.org/10.1039/d6sc05970h

Source records

pubmed · retrieved 2026-11-10T07:40:45.119Z