Computational references are not experiments: pre-registered validation of machine-learned sodium-cathode voltages
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-registere...
| Authors | Krishna Teja Vepa |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 19 June 2026 |
| Last updated at source | 29 June 2026 |
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