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Integrity report for Machine learning via artificial neural networks coupled with density functional theory and experiments for thermodynamic optimization of high-entropy alloys for hydrogen storage at room temperature

A machine-verified record of the checks Pith has run against this paper: detector runs, findings, signed bundle events, and canonical identifiers.

arXiv:2606.04803 · pith:2026:UQCCBRR5MAMFVJCXDAELIQXMRS

0Critical
0Advisory
4Detectors run
2026-06-05Last checked

Paper page arXiv integrity.json bundle.json

Detector runs

claim_evidence completed v1.0.0 · findings 0 · 2026-06-05 03:29:14.153922+00:00
citation_quote_validity skipped v0.1.0 · findings 0 · 2026-06-04 09:51:06.775280+00:00
cited_work_retraction completed v1.0.0 · findings 0 · 2026-06-04 07:27:19.702106+00:00
ai_meta_artifact skipped v1.0.0 · findings 0 · 2026-06-04 01:35:37.426547+00:00

Findings

No public integrity findings for this paper.

Signed record

The machine-readable record for this paper lives at /pith/UQCCBRR5/integrity.json. Pith Number bundles also include signed pith.integrity.v1 events where a Pith Number exists.