Computational references are not experiments: pre-registered validation of machine-learned sodium-cathode voltages
This paper demonstrates that machine-learning screens for sodium-cathode voltages, which rely on computationally derived references with systematic errors, failed pre-registered validation against experimental data due to a dominant 0.54 V bias in the reference values rather than the model itself, leading the authors to retire the screen and commit to a new calibration audit.