Computational references are not experiments: pre-registered validation of machine-learned sodium-cathode voltages
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Condensed Matter > Materials Science
arXiv:2606.23725 (cond-mat)
[Submitted on 19 Jun 2026]
Title:Computational references are not experiments: pre-registered validation of machine-learned sodium-cathode voltages
Authors:Krishna Teja Vepa
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Abstract:Machine-learning screens for battery materials are trained and judged almost entirely against computed reference voltages, and those references carry their own systematic errors. We report a case in which this matters quantitatively: our own screening stack (a graph-network voltage screen, a prior-art triage layer, and a local PBE+U bench) fails pre-registered validation against experiment-anchored literature values. Verdict thresholds, failure modes, and the primary metric were committed before analysis. On an operator-audited set of known Na-ion cathodes (n = 6 after one documented exclusion; verdict unchanged at n = 7), the raw held-out mean absolute error was 0.67 V, the pre-registered conservative metric, the upper 95% confidence bound of the cross-validated bias-corrected error, was 1.09 V, and the residual was strongly voltage-dependent (r = -0.94), so no additive calibration is valid. On the two compounds where prediction, database reference, and experiment could all be compared, the Materials Project PBE+U reference sat about 0.54 V below measurement: the reference, not the model, dominated the error. A prior-art screen found at least 70% of the targeted Na substitution space already published. We retire the screen, bound what "verified" means for our DFT ledger, and pre-register a calibration audit of it against four benchmark Li couples.
| Subjects: | Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.23725 [cond-mat.mtrl-sci] |
| (or arXiv:2606.23725v1 [cond-mat.mtrl-sci] for this version) | |
| https://doi.org/10.48550/arXiv.2606.23725
arXiv-issued DOI via DataCite
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View a PDF of the paper titled Computational references are not experiments: pre-registered validation of machine-learned sodium-cathode voltages, by Krishna Teja Vepa
Ancillary-file links:
Ancillary files (details):
- BIB_AUDIT.md
- CLAIMS_MAP.md
- D1_PREREGISTRATION_2026-06-10.md
- STEP3B_LITEXP_VERDICT_2026-06-09.md
- STEP3B_OPERATOR_VALIDATION_2026-06-09.md
- bib_verify.py
- build.sh
- consistency_check.py
- curated_na_cathodes.csv
- d1_offset_analysis.py
- fig1_parity.py
- fig2_residuals.py
- fig3_decomposition.py
- gnome_screen_summary.json
- gnome_screen_summary_structuredb.json
- gnome_triage_probe_summary.json
- make_tables.py
- preregistration_2026-06-08.md
- q1_oos_summary.json
- q2_collapse_summary.json
- qme_style.py
- s3b_litexp.py
- s3b_litexp_results.csv
- s3b_litexp_summary.json
- step2_bias_corrected.py
- step2_per_compound.csv
- step2_summary.json
- step3_curation_log.csv
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