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. 2026 Oct 12. Online ahead of print. doi: 10.1039/d6ra08367f

Table 2. Recommendations for applying machine learning to chemistry data compiled from the published literature, with the supporting evidence from this study.

Stage Recommendation Evidence in this study
Data compilation Verify every compiled value against the original source and keep a correction log 185 corrections for 181 records during the source audit, including 38 conversions from atomic to mass percent and the exclusion of one conversion-electron measurement (Section 2.1; SI Methods S1.3)
Record how the literature was searched, including any filters, and assess what the search could not reach The original search log was not preserved (Section 2.1); the retrospective audit was therefore fully documented, and its open-access recall was 12.5% for publications before 2010 and 47.8% for later publications (Section 4.5; SI Table S1f)
State who made eligibility and grouping decisions and which rules were applied All decisions made by the authors from the sources under stated rules; retrospective states documented individually (Section 2.1; SI Table S1d and e)
Target definition Define the target from measured quantities and test its sensitivity to any threshold Operational high-field fraction above 10 T; R2 of 0.422, 0.422 and 0.416 at 5, 10 and 15 T (Section 2.2; SI Table S2)
Validation design Check whether records with different identifiers originate from the same experiment, and define the unit of independence before splitting the data 45 publications formed 37 provenance groups; 11 publications belonged to three shared specimen series (Section 2.3)
Apply the same grouping in model selection as in model evaluation, and report how performance changes across validation designs With grouping applied at both levels, median R2 was 0.670, 0.322 and 0.287 for state-random, publication-disjoint and provenance-disjoint evaluation (Sections 2.6.1 and 3.2)
Model comparison Include a simple, physically motivated baseline The calibrated martensite-start comparator reached a pooled R2 of 0.354, which made it possible to test whether the flexible model (0.422) adds information (Sections 2.4 and 3.6)
Report group-balanced metrics with confidence intervals alongside pooled metrics The flexible model had the lower error in only 17 of 37 groups; ΔMAE −1.73 pp (95% CI −7.87 to 4.52) (Section 3.6)
Uncertainty Report prediction intervals under the same grouping, with both coverage and width Mean coverage of 90.2% with a median interval width of 92.1 pp on a 0 to 100 pp scale (Section 3.7)
Reporting Specify analyses and decision rules before running them, and report hypotheses that were not supported Two prespecified hypotheses not advanced (Section 3.8; SI Note S1)
Release the curated data, correction log, group assignments and code Public repository with integrity checksums (Section 6.5)