Machine Learning in Non-fullerene Organic Solar Cells: Accelerating Discovery, Design, and Understanding.

Das B, Mondal A

Open source

DOI: 10.1021/acsomega.6c01194
Published: 2026 May 26
Container: ACS omega
Publisher: Not recorded
Open access: yes

Credibility signals

uncertain Score 53/100 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
supportingDirectory of Open Access Journals: A matching record was returned by DOAJ. 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:10.1021/acsomega.6c01194,
  title = {Machine Learning in Non-fullerene Organic Solar Cells: Accelerating Discovery, Design, and Understanding.},
  author = {Das B and Mondal A},
  year = {2026},
  journal = {ACS omega},
  doi = {10.1021/acsomega.6c01194},
  url = {https://doi.org/10.1021/acsomega.6c01194}
}

RIS

TY  - JOUR
TI  - Machine Learning in Non-fullerene Organic Solar Cells: Accelerating Discovery, Design, and Understanding.
AU  - Das B
AU  - Mondal A
PY  - 2026
JO  - ACS omega
DO  - 10.1021/acsomega.6c01194
UR  - https://doi.org/10.1021/acsomega.6c01194
ER  -

APA

B, D., & A, M. (2026). Machine Learning in Non-fullerene Organic Solar Cells: Accelerating Discovery, Design, and Understanding.. ACS omega. https://doi.org/10.1021/acsomega.6c01194

Source records

pubmed · retrieved 2026-09-26T01:43:18.993Z
europe-pmc · retrieved 2026-09-26T01:43:19.005Z
doaj · retrieved 2026-09-26T01:43:19.025Z