Work

Validation design and experimental provenance shape apparent machine-learning transferability in legacy Mössbauer spectroscopy

Year: 2026

Type: article

Abstract: by more than a factor of two, and a pooled advantage need not hold when each experiment is weighted equally. We provide practical recommendations for machine learning on literature-derived chemistry data and release the curated corpus, provenance assignments and code.

Source: RSC Advances

DOI: 10.1039/d6ra08367f

Authors: Osman Armağan, Muhammed Emin Bedi̇r

Institutions: Karamanoğlu Mehmetbey University, University of Wisconsin–Madison

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,042

OpenAlex ID: W7221555716